Menopause that occurs before the age of 45 and is not medically induced  affects around one in 10 women and has serious health consequences. These consequences include increased risk of all-cause mortality, cardiovascular disease, osteoporosis, and type 2 diabetes.We investigate risk factors for the onset of natural menopause before the age of 45 in two population-based prospective cohort studies in Britain: the 1958 cohort following 8959 women and the 1970 cohort following 8655 women. These studies follow women from birth to adulthood, and we use harmonized data on birth and early life characteristics, reproductive health, health behaviour, and socioeconomic characteristics for 6805 women who were pre-menopausal, peri-menopausal or had undergone natural menopause. Of these 6805 women, 3614 participated in the 1958 cohort (of which 368 had early menopause) and 3191 participated in the 1970 cohort (of which 206 had early menopause). Taking a life course approach, we focus on three distinct life stages - birth/early life, childhood, and early adulthood - to understand when risk factors are most harmful. Respecting the temporal sequence of exposures, we use a series of multivariable logistic regression models to estimate associations between early menopause and each potential risk factor adjusted for confounders.We find that early menopause is influenced by circumstances at birth. Women born in lower social class families, whose mother smoked during the pregnancy or who were breastfed 1 month or less were more likely to undergo early menopause. Early menopause is also associated with poorer cognitive ability and smoking in childhood. Adult health behaviour also matters. Smoking is positively correlated with early menopause, while regular exercise and moderate frequency of alcohol drinking in women’s early thirties are associated with reduced risk of early menopause. The occurrence of gynaecological problems by women’s early thirties is also linked to early menopause.We demonstrate that characteristics at different periods of life are associated with early menopause. Some of these associations relate to modifiable behaviours and thus the risks of early menopause and the adverse health outcomes associated with it may be preventable.The online version contains supplementary material available at 10.1186/s12905-022-02021-4. It maPast research suggests that the onset of natural menopause is strongly influenced by genetics , 15, butThe use of a life course approach for studying the timing of menopause is supported by the literature –20. The Previous research has suggested that restricted fetal growth, generally marked by low birthweight, may adversely affect the peak number of ovarian follicles and thus affect the timing of the menopause . SeveralPoor nutrition has also been linked to the timing of menopause, with research showing that children who were not breastfed or breastfed for short periods, who had a low nutrient intake and slow post-natal growth may undergo menopause earlier , 30. BreIt has been hypothesised that prenatal exposure to cigarette smoke may affect the follicle pool by altering the development of ovarian follicles . A negatIt is hypothesised that both young and old maternal age at birth may increase the risk of earlier menopause in the daughter . If the Several studies have indicated an association between disadvantaged childhood socioeconomic position and earlier menopause, and the impact of socioeconomic disadvantage has been considered to act through the effects of hardship experience throughout life and health behaviours , 37, 38.Psychological health in early life has been linked to the onset of menopause, potentially acting though the function of the hypothalamic-pituitary-adrenal axis, which affects the reproduction function . For exaSeveral studies have found that lower cognitive ability in childhood is associated with early menopause with earlier measures of cognitive ability being more strongly related to age at menopause than tests taken later in life , 39, 40.It has been hypothesized that fewer ovulatory cycles, crudely estimated by events contributing to a lower number of menstrual cycles, such as late age at menarche, longer menstrual cycles, and a higher number of full-term pregnancies, as well as length of use of oral contraceptives where menstrual cycles are likely anovulatory, result in later age at menopause . The eviGynaecological problems are another potential risk factor for early menopause. UK Biobank data showed no evidence of an association between such problems and early menopause . HoweverBehavioural factors such as smoking, drinking and exercise affect endocrine function and women’s reproductive hormones, and thus may have an impact on the timing of menopause –52. It hAssociations between body mass index (BMI) and obesity with ovarian reserve and menopause have been inconsistent and the mechanisms underlying the associations unclear , 64–68. It has been suggested that the lack of consensus on the factors affecting the timing of natural menopause could be explained by methodological differences, e.g., differences in study populations and designs, differences in the definition of menopause, and varying use of controls in the analyses , 13. AnaWe contribute to this literature with a study investigating non-genetic risk factors from across the life course for early natural menopause using data from two population-based prospective cohort studies of British women born in 1958 and 1970 and followed from birth through to childhood and adulthood. Using data from two studies increases the numbers experiencing menopause before the age of 45 which provides greater statistical power while also allowing assessment of consistency of associations across generations. Taking a life course approach, we investigate whether early life exposures , reproductive characteristics , health behaviour , socioeconomic characteristics , cognitive ability, psychological distress and BMI are associated with early menopause. Respecting the timing and temporal order of exposures, we investigate the role of exposures during potentially critical or sensitive periods in early life alongside adult factors to understand the stages of life where exposure is important. There is substantial literature that the developmental origins of health lie in the early life period –73, and What birth and early life characteristics are associated with menopause before 45 years of age?What childhood characteristics are associated with early menopause, while factors at birth are accounted for?And lastly, what adult characteristics influence early menopause, while early life and childhood factors are accounted for?This study investigates non-genetic risk factors for early natural menopause at three distinct stages of life preceding the menopause onset – birth and early life, childhood, and early adulthood. We address the following research questions:The data used in this study is harmonized pooled individual level data comprising women from the 1958 National Child Development Study (NCDS) and the 1970 British Cohort Study (BCS70). The NCDS and BCS70 are prospective birth cohort studies that follow the lives of 8959 women  and 8655 women , respectively, born in England, Scotland, and Wales in a single week of March 1958 and March 1970 , 75. SinMenopause status is determined using information collected at age 42 and 46 in BCS70 and age 44/45 and 50 in NCDS. Of the 8655 women in the 1970 cohort at birth, 5117 participated at the age 42 follow-up survey (of 6600 eligible for interview) and 4427 participated at the subsequent age 46 survey (of 6171 eligible). Of the 8959 women in the 1958 cohort at birth, 4712 participated at the age 44/45 follow-up survey (of 6606 eligible for interview) and 4968 women took part at the subsequent age 50 survey (of 6139 eligible)  and the reason for amenorrhea. All women were asked about changes in the regularity of their menstrual periods in the last few years or before their FMP, about having undergone hysterectomy or bilateral oophorectomy, and hormone therapy (HT) use, including the dates of surgery or initiation of HT, and FMP prior to initiation of HT if menstruation has ceased before the HT start.The derivation of menopause status followed widely accepted classification , 22. NatThe choice of potential risk factors is guided by the literature reviewed above. Risk factors at birth include: father’s social class, maternal smoking during pregnancy, birthweight standardized for gestational age, maternal age, and breastfeeding. Apart from birth weight and gestational age, which were recorded by a midwife, circumstances at birth were reported by mothers of cohort women. Childhood risk factors include: cognitive ability at age 10 in BCS70 or 11 in NCDS (assessed by reading comprehension and mathematics test scores), age at menarche, and a set of health behaviour characteristics at age 16 – smoking, alcohol consumption, frequency of physical activity, BMI, and psychological distress . At ageDescriptive statistics for all variables included in the analysis for each cohort separately, including means and standard deviation, median and range for continuous variables, and percentages with 95% confidence intervals for categorical variables, are presented in Supplementary material . We use multiple imputation techniques to tackle data missingness see . RespectThe pooled cohort analysis enables us to estimate associations with increased statistical power  and focus on the common factors affecting menopause before 45 (rather than associations observed in each study separately). The sequential regression modelling, adjusted for preceding factors only (as confounders) and not mediators, aims to estimate (adjusted) associations which may reflect causal effects (under assumptions of no unmeasured confounding or other sources of bias) , 85.In secondary analyses, we carried out logistic regression analyses relating menopause before 45 to the birth, childhood, and early adulthood variables, in each cohort separately. These results are presented in Supplementary material  with chained equations –88, undeThe analytical sample comprised 6805  menopausal, peri- and pre- menopausal women (3191 participating in the 1970 and 3614 participating in the 1958 cohort). By age 45, 206  women in BCS70 and 368  women in NCDS analytical samples had undergone natural menopause. The pooled sample comprised of 574  early menopausal women. Women’s birth, childhood, and adult characteristics, in each cohort separately, are presented in Supplementary material  which found no effect on menopause before 45 years . This, aThere are limitations to consider in our study. As with any longitudinal study, both cohorts have been affected by attrition. Fortunately, the retention rates over the decades following the study members have been strong . The proOther limitations include the retrospective collection of information on menopause and potential for recall bias although recall was not over a long period. Previous research has indicated that menopause is reasonably well recalled but the recall error is expected to increase with the time after menopauseWe investigated the role of a large number of potential risk factors at different stages of life, and with different levels of (unit and item) missingness. We noted missingness in risk factors between 4 and 31% in the 1958 and 3–57% in the 1970 cohort  is inevitable. We do not account for the variation in the experience of menopause before 45 years among women of different ethnic backgrounds because both cohorts are of predominantly White ethnic background and very few women (less than 10) of non-White ethnic background have undergone early menopause in our analytical sample.Our analysis investigates common risk factors for women born 12 years apart which may obscure cohort-specific relationships. We use a combined study because of the comparable study designs and measures but acknowledge the potential for bias in the harmonized data that may result from combining data from different studies  which may in turn affect the generalizability of our findings. Nonetheless, it is reassuring that the associations presented here are consistent with those observed in each cohort separately. If statistical significance is not reached in a cohort, the estimated effects remain in the same direction  in two population-based prospective cohort studies of women born in Britain in 1958 and 1970 and followed from birth to adulthood. We demonstrated that characteristics at different stages of life are associated with menopause before the age of 45 – an event with serious health consequences. Some of these associations may reflect genetic or underlying biological processes which with further investigation could lead to better understanding of the menopausal process and reproductive health across the life course. Further, some of the characteristics associated with early menopause relate to behaviours which are modifiable . If the presented associations reflect causal effects, early (timely) prevention may reduce the risks of early menopause and hence the adverse health outcomes associated with it.Additional file 1: Supplemetary material. Risk factors for natural menopause before the age of 45: evidence from two British population-based birth cohort stuides. Harmonised variables used in the analysis (Table 1 Menopause status); classification of women into menopause status ; descriptive statistics for all variables included in the analysis for each cohort separately (Table 3 Descriptive statistics); univariate and multivariate study specific  and pooled (Table 4 Pooled models) regression models.


This quality improvement study establishes an 8-component framework to assess surgical improvement efforts that are reported to the American College of Surgeons. We tried to identify an appropriate and adequate framework to evaluate these improvement efforts; however, no single framework for small-scale, clinician-led improvement efforts exists. Current available frameworks focus on investigative or research-based efforts, efforts led by improvement specialists, or large-scale  improvements. Herein we describe the development of the ACS Quality Improvement Framework, whose function is to evaluate small-scale surgical improvement efforts.The American College of Surgeons (ACS) Quality Programs collect more than 3500 improvement efforts annually. These efforts are usually local  or small scale 5) was initially conducted to identify framework components and associated criteria thought to be applicable to small-scale surgical improvement efforts. With identified framework components, a nominal group technique was conducted6 with 3 rounds of iterative prototype development and pilot testing on a sample of improvement efforts. Each round prioritized development on 3 constructs: content validity, face validity, and feasibility. Prototype frameworks for each round were evaluated for feasibility using a split sample of ACS Quality Program improvement efforts. The final framework also underwent pilot testing for reliability by individual committee member evaluation.In this quality improvement study, framework development was conducted by the 12-member ACS Quality Programs Advisory Improvement Committee, which consists of surgeon or ACS staff representatives from 7 ACS Quality Accreditation Programs . Each committee member has extensive experience in leading and supporting surgical improvement efforts. A review of published frameworks in 3 areas  selection; the final framework consisted of 8 components with 39 criteria in total , Box. ThBox. Problem statement: defined problem statement presents a clinical reason to pursue the projectLocal issue: problem is known to be a local issue, not just a broader problem that may or may not be a local issueLocal data: problem is defined with data describing the local problem Significance of problem: significance of the problem is explained Patient facing: project involved patient input regarding problem delineation or detailing to be patient centeredImprovement team input: project improvement team involved with defining and identifying problemInternal stakeholders: internal stakeholders involved in defining and identifying problem; can include individuals such as doctors and nurses who are not part of the project improvement teamExternal stakeholders: stakeholders external to project team and internal stakeholders involved in defining and understanding problem Specific: project goal is specific Measurable: project goal is measurable Achievable: project goal is achievable Relevance: project goal is relevant Timely: project goal is time driven Improvement planning: planning of the project that describes the strategy being undertaken Strategic rationale: included in the project planning is the rationale for the chosen strategy Stakeholder involvement: stakeholder involvement and input into planning of the strategy Resources: information needed to operationalize the project Data: use of data planned before start of project Probable limitations: probable or possible limitations, challenges, or hurdles identified a priori (during planning stages and before project begins)Contextual issues: identified to successfully fulfill the strategy Description of project execution: methods described, specifically of implementation strategy Evaluation during implementation: the implementation of the improvement effort is evaluated Problems encountered: the barriers and problems are identified throughout the project from launch to completionChanges undertaken: the changes undertaken in the method, implementation, strategy (other) in response to barriers and problems are identified and capturedData: used to evaluate processes Stakeholder involvement: achieved to inform evaluation of project processes, protocols, and strategiesData and analytics: used to measure effectiveness of the effort and achievement of project goalsOutcome quantification: project outcome is quantifiedGoal achievement: improvement team determines whether the specified project goals were achieved fully or partially or were not achievedBiggest limitations: limitations in determining the outcome are identified in review of the entire effort after completionUnintended consequences: identified and capturedStakeholders: made aware of outcomesCost of project: costs are recorded Value: value of doing the project is known Stakeholders’ perspective: stakeholders assess value of the project, taking into account resources, processes, and outcomesLessons: lessons capturedCurrent and future action: any actions mentioned regarding use  knowledge acquired and lessons learnedFuture actions: decision on whether the effort will continue as is, be expanded to other areas, continue only with revision, be stopped, or otherSurveillance plans: plans for surveillance for recurrence of the problem are statedFTE indicates full-time equivalent .We developed a quality improvement framework to evaluate surgical improvement efforts reported to the ACS. It was specifically developed for and tested on small-scale surgical improvement efforts collected by sites participating in ACS Quality Programs. The planned function for the framework is to evaluate improvement efforts submitted to the ACS—with 2 goals: (1) ascertain how well the efforts were conducted; and (2) identify gaps in execution, aiming to advance improvement efforts.The framework is unique for 2 reasons. First, to our knowledge, it is the first that specifically focuses on small-scale efforts—meaning it purposely addresses the type of improvement efforts largely seen in surgery in which local efforts that are clinically important are also often low resourced, unfunded, occurring in single settings like a ward or unit, and conducted by busy frontline surgeons. Second, this framework eliminates issues more relevant to large-scale or investigative research efforts yet maintains core items, such as problem identification, identifying project aims, and use of data—all important to frontline efforts.The framework also can be used to pragmatically guide conduct of a small-scale improvement effort—the planning, conducting, middle- and end-of-project evaluation, and reporting. Efforts are in progress to incorporate this framework into ACS Quality Programs to help frontline clinicians better execute improvement efforts, and we believe this framework will support these efforts.


Increasing the career success of scientific and technological innovation talents has become an important means of keeping and using talents in countries around the world. However, the problem has not been solved effectively.Thirty-five cases were chosen in this study. The combined effects of human capital, psychological capital, micro-social capital, team social capital, and macro-social capital on the career success of scientific and technological innovation talents, as well as relevant influencing mechanisms, were discussed using qualitative comparative analysis (QCA).Results demonstrate that: (a) while a single factor cannot be the only condition for the high career success of scientific and technological innovation talents in universities, increasing macro-social capital plays a relatively universal role in a high career success rate. (b) There are three paths driving the high career success of scientific and technological innovation talents in universities. (c) There are two paths driving the non-high career success of scientific and technological innovation. Both have characteristics of absent psychological capital and micro-social capital. Moreover, the driving mechanism of high career success has an asymmetrical causal relationship.Research conclusions are not only conducive to expanding the research perspectives of social capital theory and career success, but also provide valuable insight into how to stimulate the career success of scientific and technological innovation talents in universities. Scientific and technological innovation is an important indicator of scientific and technological strength, and even the comprehensive strength of a country or a region. It is the core competitiveness of national development and plays a critical role in improving the scientific and technological innovation ability of a country. As the main carriers of scientific and technological innovation, scientific and technological innovation talents in universities are the valuable wealth of a country or a region. Training and retaining scientific and technological innovation talents have become the primary task for countries to implement innovation-driven strategies and promote high-quality development. Talent development is closely related to career planning. It is necessary to promote the career success of scientific and technological innovation talents in order to train and retain them. Universities are the cradle of scientific and technological innovation talents and an important base for the transformation of scientific and technological achievements. The career success of university scientific and technological innovation talents, especially the objective career success, is directly reflected in the output of scientific and technological achievements. Therefore, it is of direct and important significance to study their career success stimulation paths in universities to promote scientific and technological progress.Studies on career success can be divided into two parts: one is studies on the conception and measurements of career success, the other part is on its the influencing factors.Studies focusing on career success started in the 1980s. At that time, scholars believed career success to be the “positive psychological feeling or related work achievement that one person has accumulated gradually ”. Such aThe late 1990s ushered in the era of a knowledge-driven economy, and changes in external environments, especially organizational environments, profoundly influenced the career life of employees. The contractual relationships between organizations and employees are not single and fixed. The mobility and unpredictability between employees and organizations increased, and the era of “Boundaryless Career” arrived . Under tAdditionally, There are many studies on the influencing factors of career success. Many empirical research results and meta-analysis studies have proved that relevant demographic variables , human c(1) Human capital influences career success. Relevant scholars have been testing the influences of human capital on career success through empirical studies since the 1990s. (2) Psychological capital influences career success. Psychological resources theory  believesCurrently, many studies discuss the relationship between psychological capital and career success independently. The job performance of employees may influence objective career success directly, and job satisfaction is an important indicator of career success . Larson (3) Social capital influences career success. Hananifan used the term, “Social capital” for the first time . He elabIn Making Democracy Work: Civic Traditions in Modern Italy, To sum up, existing studies on the influencing factors of career success mainly focus on one or several simple factors. However, these studies only conducted dispersed studies of career success using single or several simple variables. The reliability of such research conclusions and their promotion and application values in management practices have decreased . As per The QCA was introduced innovatively in this study. Influencing factors of the career success of scientific and technological innovation talents in universities were summarized from the perspective of capital theory. The combinations of influencing factors that generate high career success rate as well as the interaction relations of factors in the combination were recognized using tracing logics. This provides a granularity analysis of the relations of career success with human capital, psychological capital, and social capital.In conclusion, this study focuses on two causal relationships: (1) whether human capital, psychological capital, and social capital are essential conditions for scientific and technological innovation talents to achieve a high career success rate. (2) How to couple these capital elements to achieve high career success rate of scientific and technological innovation talents. The theoretical model in this study is shown in Traditional quantitative analyses typically believe that independent variables are mutually independent and establish regression equations on this basis for the linear test of causal relationships. In fact, variables in the career success of scientific and technological innovation talents influence mutually rather than being independent. They affect the career success of scientific and technological innovation talents in universities together. On this basis, qualitative comparative analysis (QCA) was applied as the major research method in this study.Qualitative comparative analysis was proposed by Ragin, a sociologist, in the 1980s. Unlike the traditional quantitative analysis, QCA emphasizes the analysis of a combined effect rather than a net effect. Through cross-case comparison, researchers can recognize the combination of conditions that may lead to the occurrence or non-occurrence of outcomes, thus enabling them to find logical relations between condition matching modes and outcomes. Therefore, QCA can further recognize the collaborative effect of multiple conditional variables under the admission of complexity of causality . CompareQualitative comparative analysis includes fuzzy set qualitative comparative analysis (fsQCA), multi-value set qualitative comparative analysis (mvQCA), and clear set qualitative comparative analysis (csQCA). Among them, mvQCA and csQCA only apply to category problems with discontinuous data. However, fsQCA does not only process category problems, but is also used to process some membership problems in this study. As a result, fsQCA was chosen as the research method in the present study.Based on the case selection requirements of the QCA research method, the homogeneity of the background information of the cases is used to reduce the influence of the control variables, and the variability of explained variables between cases results in different configurations. Besides, Central Committee and The State Council on Further Strengthening Talent Work identified the talents as people with some knowledge or skills to make creative labor activities; promote the development of socialism, material civilization, political civilization, and spiritual civilization; and make positive contributions to building socialism with Chinese characteristics. According to this regulation, talents with good scientific and technological innovation ability, who participate in scientific and technological innovation activities directly, and make important contributions to technological development and social progress, are the scientific and technological innovation talents . With reThe principles of scale selection in this study are as follows: first, the scale content is highly compatible with the scenario of this study; second, the scales have high reliability and validity in the reviewed literature.The measurement of career success adopted the scale developed by The Cronbach’s coefficients of the two sub-scales were 0.739 and 0.753, respectively, indicating high reliability. In addition, this questionnaire includes not only objective indicators (career competitiveness), but also subjective indicators (career satisfaction), which is more in line with the career success concept in the theory of the “Boundaryless Career.” Therefore, this study adopted this scale to measure the variable of career success.At present, the most commonly used human capital measurement questionnaire in academia is the human capital characteristic scale designed by Psychological capital is a positive psychological state manifested by individuals in the process of growth and career development, and mainly includes four aspects: self-confidence, optimism, resilience and self-efficacy . The psyReferencing Brown’s studies, social capital was divided into three dimensions, including microscopic dimension, mesoscopic dimension, and macroscopic dimension. These three dimensions correspond to individual social capital, team (organization) social capital, and macrosocial capital.In order to be closer to the context of Chinese human relations society, this paper adopted the individual social capital scale developed by Team social capital refers to a resource exchange capability embedded in the internal social network of team members. It mainly reflects the opportunity, willingness and ability of resource exchange. In order to better reflect the team social capital in the Chinese context, this paper adopted the team social capital scale of Macro-social capital refers to the degree of individual perception of the norms, trust and other characteristics of the country. Measurement scales of macro-social capital are relatively rare. In order to be closer to the definition of macro-social capital in this paper, we adopted the measurement method in the World Trust Survey (WVS) and the CGSS survey to measure macro-social capital. That is: “Do you think most of the people around you can be trusted?” Likert six-point method was used to evaluate.For the adopted foreign scales, in order to avoid the differences caused by semantic differences and cultural backgrounds, this study invited doctoral students in English major to perform translation-back translation. Before the formal investigation, five graduate students and five teachers were invited to conduct pre-investigation. The questions they brought up and possibly ambiguous sentences were adjusted again. The specific items finally determined are shown in Variable calibration is a special process of fsQCA, helping to transform the original variables into continuous membership fractions within 0–1. With reference to previous studies , the totA superset of outcomes is a requirement. If a requirement is included in the truth table for analysis, it might be simplified as a “logic remainder term.” Requirements have to be first analyzed before the combination analysis . Table 4The combinations of conditions that attribute the high career success of 35 samples were analyzed using fsQCA3.0 software. These different combinations reflect different capital environments to realize the same outcome (high career success degree). According to The combinations of outcomes were determined according to the intermediate solution. The center conditions and contributing conditions of each combination were gained by embedding the relations of the parsimonious and intermediate solutions . In QCA,H1, H2, H3, and H4 are sufficient conditions for scientific and technological innovation talents to achieve employee career success. In QCA, the overall coverage of solutions reached 0.734, indicating that these four paths explained the major reasons for the career success of most scientific and technological innovation talents . These f(1) H1 is the individual-dominated path based on social trust. It indicates that scientific and technological innovation talents in universities have good individual qualities  and high trust in society . If microsocial capital also exists, scientific and technological innovation talents can achieve high employee career success. For individuals, microsocial capital, i.e., the network relationship of individuals, is an important factor in achieving career success .In conclusion, the microsocial capital of individuals plays an important role in their career life. Some scientific and technological innovation talents have high quality, high irreplaceable ability, good psychological quality, and generally good trust in the society. Microsocial capital decides the career success of these individuals. Individual relations, especially the “weak relation,” decide whether they can achieve career success .(2) H2 is the path driven by individual capitals and social trust under the assistance of platforms. Given the high team social capitals, scientific and technological innovation talents with good individual qualities  and high trust in the society  can achieve high career success even when there is no high microsocial capital. If scientific and technological innovation talents join or form an excellent team, they can accomplish the team goal and improve the team performance through division of labor, cooperation, and knowledge-sharing in the team. In this case, individuals with high fitting with the organization can improve their job performances through high team performances, thus realizing the ultimate goal of employee career success.In other words, a scientific and technological innovation team tends to accept talents with good human and psychological capital, high irreplaceable ability, and outstanding personal ability. These talents generally conform to the “individual-organization ability agreement and organizational supply-individual demand agreement.” The high fitting between individuals and organizations positively influences the job performances of scientific and technological innovation talents, thus enabling them to achieve employee career success. This also reflects the necessity for the coexistence of high human capital, high psychological capital, high team social capital and high macrosocial capital in H1. Additionally, high human capital, high psychological capital, and high macrosocial capital exist as center conditions, while team social capital exists as a contributing condition. This indicates that scientific and technological innovation talents who conform to this path pay prior attention to improving their personal abilities.(3) H3 is the path driven by human capital and social capital under the assistance of social trust.  H3 demonstrates that scientific and technological innovation talents can achieve career success if high human capitals and high social capitals  coexist. This mutually corroborates with the job demand-resource (JD-R) model . The JD-The “double-path hypothesis” of the JD-R model believes that continuous job requirements may consume individual resources and energies and cause losses, thus resulting in negative influences, such as a series of job burnout. The acquisition of job resources may stimulate the job motivation of employees and thereby induce the gain process, thus improving job efficacy . ScientiIn the dual-path hypothesis in the JD-R model, the buffer hypothesis and response hypothesis verify the reasonability of H3. Given the high human capital, scientific and technological innovation talents can achieve career success if there is high social capital  at the same time.(4) H4 is the path where psychological capital and social capital act together. Under the coexistence of high psychological capital and high social capital , scientific and technological innovation talents can achieve employee career success, no matter what the human capital is. A positive psychological state positively influences social resources accumulated by individuals . Turban In conclusion, psychological capital positively influences individual social capital. As the intermediate variable, social capital also positively influences the career success of individuals. This proves that the coexistence of high psychological capital and high microsocial capital in H4 leads to career success.Besides, most samples of H4 were aged between 30 and 40, and their years of working were less than 7 years. According to H1 and H2 form the second-order equivalent combinations of four paths. The coverage of H4 reached 62.8%, ranking the top in the four paths. In other words, most scientific and technological innovation talents achieve career success through H4. This fully proves the important effects of psychological capital and social capital  on employee career success. In H4, the effect of human capital is not obvious. In the era of knowledge economy, traditional human capital has no advantages of competitiveness . In pracPaths generating non-high career success were tested in this study. There were two paths generating non-high career success (NH1 and NH2). NH1 shows that individuals cannot achieve career success even if they have high team social capital and macrosocial capital since there is no human, psychological, and microsocial capital. NH2 shows that under the absence of psychological capital and social capital, individuals cannot achieve career success no matter how high the human capital is. It was found that both NH1 and NH2 are characteristic of the coexistence of non-high psychological capital and non-high microsocial capital. In other words, individuals may find it impossible to achieve career success if they have insufficient psychological capital and the microsocial capital is not advantageous, no matter how high the human capital, team social capital, and macrosocial capital are.The robustness test is an important link of QCA . The preHuman capital, psychological capital, microsocial capital, team social capital, and macrosocial capital were integrated using combination thinking and QCA. The multiple concurrence factors and complicated influencing mechanism of the career success of scientific and technological innovation in universities were discussed. Some major conclusions were drawn, and are listed as follows:1. There are four paths to generate the high career success of scientific and technological innovation talents in universities:(1) Human capital*psychological capital*microsocial capital*macrosocial capital: the combination of high human capital, high psychological capital, high microsocial capital, and high macrosocial capital; (2) human capital*psychological capital*team social capital*macrosocial capital: the combination of high human capital, high psychological capital, high team social capital and high macrosocial capital; (3) human capital*microsocial capital*team social capital*macrosocial capital: the combination of high human capital, high microsocial capital, high team social capital, and high macrosocial capital; (4) psychological capital*microsocial capital*team social capital*macrosocial capital: the combination of high psychological capital, high microsocial capital, high team social capital and high macrosocial capital.This shows that under the framework of career success driven by the three capital theories, there is no single best path for the career success of scientific and technological innovation talents in universities. Paths that trigger the same outcome are conditional and multidimensional. In the management practice of universities, managers need to first identify some of the three capital conditions that they already have, then compare the four stimulation paths, and finally supplement another part of the capital conditions, and seek the least resources to stimulate one of the paths, so as to improve the career success efficiency of the scientific and technological innovation talents in universities.2. There are two paths to generate the non-high career success of scientific and technological innovation talents in universities:(1)∼human capital*∼psychological capital*∼microsocial capital: the combination of low human capital, low psychological capital and low microsocial capital; (2)∼psychological capital*∼microsocial capital*∼team social capital*∼macrosocial capital: the combination of low psychological capital, low microsocial capital, low team social capital, and low macrosocial capital.This means that psychological capital and micro-social capital are very important for the career success of scientific and technological innovation talents in universities. When psychological capital and micro-social capital are low, no matter how other conditions match, the career success of scientific and technological innovation talents in universities cannot be stimulated. Therefore, when managing scientific and technological innovation talents in universities, they should pay attention to the cultivation of their psychological capital to ensure that they have a positive psychological state and the confidence to overcome difficulties, so that they can maintain a high professional enthusiasm and commitment. On the other hand, because the research results show that, compared with macro social capital and team social capital, micro social capital plays a more important role in the career success of scientific and technological innovation talents in universities. Therefore, in the management practice, in addition to providing team platform support for scientific and technological innovation talents, encouraging and creating opportunities to help them establish a wider personal-level social resource network plays a more important role in helping their career success.3. H4 shows the highest coverage, revealing that most scientific and technological innovation talents achieve career success by increasing psychological capital and using current surrounding social capital. Most samples conforming to this path are young people who have worked for a few years and have low titles. This indicates that scientific and technological innovation talents in the initial career stage tend to achieve career success through H4. In other words, talents join a good team and achieve career success by increasing their psychological capital, improving their social trust and using surrounding microscopic relation networks. At the same time, the marginal benefit from increasing human capital is relatively low.4. By comparing the paths, high macrosocial capital is recognized as the center condition in all four paths that generate high employee career success. Going forward, the government must view increasing overall social trust as one of its primary job objectives in improving the career success of scientific and technological innovation talents.This study has some disadvantages. First, only the influences of four important variables of three aspects  on the career success of scientific and technological innovation talents were discussed. However, other influencing factors and variables can be added in future studies to increase the universality of the research conclusions. Second, abundant qualitative data were supplemented to the discovered combinations. However, no deep qualitative analysis has been reported yet. In future, new theories of the career success of scientific and technological innovation talents can be studied in-depth by combining case studies. Finally, this study only collected static data due to data accessibility and could not collect cross-time data. With the increasing perfection and development of time series QCA, influences of capital environmental changes on the career success changes of scientific and technological innovation can be further discussed.The original contributions presented in this study are included in the article/All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.


The fundamental objective of military field training exercises (FTX) is to prepare military personnel for real-life operations through simulated scenarios. These training sessions often require extreme physical efforts with prolonged, high-intensity exercises that can be combined with food restrictions and partial, or total, sleep deprivation. Such conditions can compromise an individual’s physical performance and cause tissue damage, thus affecting their health. This study aimed to perform a systematic review of the literature to identify studies that measured the changes in hormone levels and biomarkers of cellular injury and oxidative stress resulting from FTX with high levels of energy expenditure combined with food and sleep restrictions. PubMed and the Scopus database were searched for articles that combined physical effort/food restriction/sleep deprivation with military training. The initial database search identified 158 articles that were reduced to 18 after confirmation. Significant reductions were reported in thyroid hormones, T3, T4, and anabolic hormones such as testosterone, insulin and androstenedione. An exception for GH was found, which increased throughout FTX. Less distinct responses to FTX were observed with cortisol, TSH and LH. The presence of biomarkers for cellular damage  and increased immune response activities were also described. The scarcity of information on oxidative stress, analyses of cellular injury and biomarkers of inflammatory responses warrants the future study of these topics, which could be helpful in facilitating the safe and effective physical preparations of the members of the armed forces. Military field training exercises (FTX) encompasses periodical and coordinated tactical operations with deployed units to prepare personnel for active duty. Designed to reflect the real-life challenges the armed services may encounter, the conditions simulated in an FTX involves high-intensity, prolonged, and extreme physical efforts together with food restrictions, psychological stresses, temperature variations, and sleep deprivation   , 2 . WhMuscle damage can be indicated by increases in the serum levels of specific biomarkers. These can either be related to the rupture of muscle fibers during and after training sessions, with the posterior release of its cell contents into the blood   , or to Physical activity also releases the production of free radicals that are related to the intensity of a workload on metabolic processes   . These Food restrictions and long durations of high-intensity “endurance-like” training as experienced during military field activities are contributing factors to physical damage. While endurance athletes have greater control over the repositioning of macronutrients, military units performing FTX might be limited to replenishing themselves on strict schedules or only when the opportunity allows. These limits on food consumption usually causes energy deficits that leads to more severe muscular damage and changes in serum hormone levels, such as testosterone, growth hormone (GH), insulin, cortisol, T3, T4, and TSH. Physical distress, sleep deprivation and food restrictions during FTX    ultimatInadequate sleep and resting periods observed during FTX, along with an increase in free radicals and inflammatory mediators, can increase oxidative stress and, ultimately, result in cellular damage. Sleep deprivation impairs the mechanisms that adjust the metabolism and immunological systems and the restoration of energy storage in biological systems, such as muscle glycogen   , which Considering this, it is possible to assume that the triad of intense physical demand/food restriction/sleep deprivation typically experienced during military field training exercises (FTX) intensifies the physiological stress of the participants, which can, eventually, result in adverse clinical outcomes. A commonly occurring condition is rhabdomyolysis, which is the process of massive cell destruction wherein cell contents are released into the bloodstream associated to relevant clinical conditions such as hyperphosphatemia, hypercalcemia, disseminated intravascular coagulation, compartment syndrome, and cardiac arrhythmias   , 12 .Although a variety of biomarkers from individuals who have participated in FTX sessions have been previously studied   , 13 , aSearches were performed on PubMed and Scopus databases to select articles published before December 1, 2020 using the terms: “military training” OR “military exercise” OR “physical exercise” OR “high intensity exercise” OR “caloric cost” OR “caloric expenditure” OR “energy cost” OR “energy expenditure” OR endurance OR exhaustion) AND  AND (“sleep restriction” OR “sleep deprivation” OR “sleep shortage” OR “rest deprivation”) AND .The search aimed to select publications that involved changes in biomarkers resulting from the joint interventions of physical effort/food restriction/sleep deprivation in young adult military units under training. Review studies and those using animal models were excluded. The remaining articles were independently reviewed by two members of the study (FB and TB) for inclusion or exclusion. First, the titles and abstracts were compared to exclude duplicate articles and analyzed for the elegibility criteria. Then, each was read in its entirety to scrutinize the inclusion and exclusion criteria. Those with relevant citations had their primary references hand searched and analyzed. Any discrepancies were resolved by a third reviewer (TP). The resulting process was to list the publications that fulfilled all the inclusion criteria and served the present review’s primary objective.Cochrane Risk of Bias Tool , low (L), or high (H) risk of bias. The articles’ evaluation was conservative, and a high risk of bias was related to the absence of records or uncertainty in the observation of the quality domains. Two researchers conducted the process (FB and TB), and a third one (CB) analyzed any discrepancies.Five different domains were evaluated to assess the quality and risk of bias of the selected articles according to the as Tool   , which Preferred Reporting Items for Systematic Reviews and Meta-Analyses ) recommendations  and unchanged in one (25%). GH was studied in five articles and showed an increase in all (100%) of them. In the nine studies that evaluated the level of testosterone after FTX, all (100%) of the studies reported a decrease. Only two studies reported on LH with a different response in each. The gonadal hormone levels and hypothalamus-pituitary-gonadal axis-related hormone levels  all presented lower levels in the articles reviewed, however the levels of DHEA-S showed an increase. Four studies analyzed the insulin-like growth factor fractions , and each reported lower levels after the training program. An inconsistent response in the levels of the IGF binding proteins was observed. IGFBP-I levels rose in two studies (66%) and decreased in one (33%) study. IGFBP-2 and IGFBP-3 levels appeared to be augmented, and one record of IGFBP-6 showed a decrease in its concentration.The catabolic hormone, cortisol, presented inconsistent results: two studies showed that cortisol levels increased (28%), four studies showed a decrease (56%), and one study showed unchanged levels (14%). Prolactin appeared on three occasions, and it was decreased in two (66%) and increased in one (33%). Secretin increased in both studies where it was reported (100%), while leptin levels were unchanged in one study (25%) and decreased in three (75%). Other hormone levels are presented in Two studies reported on inflammatory mediators, namely lymphocytes and neutrophils, with one study reporting an increase and a reported decrease in the other. Two studies reported an increase in monocytes and leukocytes (100%). One publication assessed the white cell count  and observed an increase in all four (100%). Another study showed a decrease in hematocrit levels (100%). C-reactive protein and TNF appeared in three articles, two reported an increase (66%) and the other reported a decrease (33%). For IL-6, three articles reported a solid increase (100%), while IL-2 levels were reported to be higher in one study and unchanged in another (50-50%). Other interleukins were measured and are shown in Few studies that included the triad of conditions also assessed the biomarkers of cellular injury. An increase of myoglobin was found in one (100%) study, and two records (100%) reported a decrease of hemoglobin. Proteins, such as SHBG and BDNF, were also found during our review, with a reported increase of the former in two articles and a decrease of BDNF in one (100%) study. Finally, this review did not retrieve a single study that considered the three interventions and biomarkers of oxidative stress.The combined scenario including prolonged physical exertion, food restriction, and sleep deprivation is directly related to physiological disturbances   . The prOur results suggests decreases in thyroid and anabolic hormone levels (except for increased levels of GH) and increases in inflammatory mediators and biomarkers for cellular injury, although the last two variables were infrequently studied by the time of this review. Additionally, there is disagreement regarding the behavior of some hormones with the triad of interventions, especially regarding the levels of TSH, LH, and cortisol, and there was no information on the biomarkers for oxidative stress that considered the simultaneous application of the three different interventions.The impact of the triad of interventions on various biomarkers might explain the changes often observed in the body composition of participants after FTX. Considering the role of thyroid hormones in protein synthesis and the regulation of cellular metabolism, their decreased levels observed after military exercise might be one of the reasons behind diminished fat free mass, fat mass, and total body mass   , 34 . HThe variations in insulin-like growth Factor 1 (IGF-1) and its related counterparts reported after FTX could contribute to the lower anabolic metabolism rates seen in this review. IGF-1 secretion occurs in the liver and is regulated by GH levels. Its circulating forms of plasma exert control over some metabolic pathways that permits the use of IGF-1 as an adequate biomarker of tissue remodeling, nutritional state, and metabolic adaptations   . In theOur study verified the direct impact of anabolism via the decrease observed in anabolic hormones. All the articles reviewed presented evidence for lower quantities of circulating testosterone, estradiol, dihydrotestosterone (DHT), insulin, and androstenedione   – 32 . OThe reported changes in cortisol levels were inconsistent between the articles reviewed. The levels appeared to be lower when the FTX included a more limited calorie intake    or a loIt is worth mentioning that most studies reporting hormonal changes presented their conclusions while only considering the statistical significance in response to stressors. There was no discussion as to whether these modifications were clinically significant or if they represented meaningful changes, such as modifying values out of their reference ranges. Only Henning and cols.    discussHigher myoglobin serum levels probably relate to muscle cell damage since it is the result of the release of muscle cell contents into the plasma after cell disruption   – 5 . OnOur review also identified a lack of inclusion of female military personnel in past studies. Consequently, there is little information on the physiological variations experienced by women during an FTX, especially considering the simultaneous application of these three interventions, which is important to preserve the simulation conditions for actual military operations. Considering the gradual yet steady increase in the recruitment of women into the armed forces, future studies should consider hormonal and biochemical responses in women in relation to FTX. The absence of data on women was not related to our literature search. While our review was limited to searches in only two databases (PubMed and Scopus), both are highly representative of our fields of interest to the present studies. The frequent absences of experimental details on the characteristics of FTX in some analyzed articles made it difficult to determine the influencing factors that could explain some of the conflicting results. Nevertheless, the generally low risk of bias of the articles reviewed suggests that the inclusion/exclusion screen was of high quality. Only performance bias remained a high-risk parameter, which can be related to the difficulties for researchers in this area in creating the ideal conditions for a double-blind study. Furthermore, most studies were conducted during an actual FTX (and not simulated interventions), which suggests that all participants experienced similar stress conditions. Although most studies did not report the actual temperatures, the descriptions of the FTX did not include thermal factors which suggests similar weather conditions.In conclusion, the joint intervention comprising prolonged physical effort, food restriction, and sleep deprivation combined during military field training exercises provoked a decrease in levels of thyroid and anabolic hormones, except for growth hormone, which increased. The other measured hormones--cortisol, TSH and LH--showed a less consistent response to FTX. The data on inflammatory mediators and biomarkers for cellular injury were insufficient to draw conclusions. Both variables increased in the few studies retrieved on the topic, which could explain the impaired physical performance due to FTX, jeopardizing the operational readiness of personnel in combat scenarios and putting individuals at risk for physical harm. Finally, this review found a lack of data about the modifications of biomarkers of oxidative stress after FTX, which prevented this research from drawing conclusions on a few physiological pathways that remain obscure to the present date. Another important fact, identified by the present review, is the lack of studies following hormonal and biochemical responses in female military units, which encourages future studies to bridge the gap.


Family connections are crucial for trauma-affected refugees from collectivistic cultures. Evidence-based family interventions are consistently promoted to support a host of mental and relational health needs of families exposed to traumatic stressors; however, there is still limited research focused on cultural adaptation and the testing of the effectiveness of these interventions on some of the most disenfranchised populations in the aftermath of forced displacement. This systematic review was conducted to examine the reach of existing evidence-based family interventions implemented with newly resettled refugees globally. Studies included in this review include those testing the effectiveness of a systemic treatment with pre and post intervention evaluation, studies with or without control groups, and studies that include at least one family member in addition to the target participants. Twelve studies met the inclusion criteria. Barriers to conducting randomized control trials with displaced refugee populations are discussed. Recommendations are made for future studies to include a focus on scientifically rigorous multi-method designs, specific cultural adaptation frameworks, and the integration of relational aspects rather than focusing only on individual adjustment. Global displacement continues to rise; therefore, it is imperative that the mental health and wellbeing of displaced populations be treated with a comprehensive, multi-level framework. Forced displacement estimates exceeded 89.3 million people globally by end of 2021 and is expected to be even higher in the future with new and ongoing global conflicts [Forced displacement disrupts the entire community and family structure of migrant and refugee populations . ExposurMost refugees belong to collectivistic societies that value family connection and interdependence . Family The COVID-19 pandemic further exacerbated existing mental and relational health issues; specifically, it created more barriers for refugee families to stay connected when they were geographically separated . ResettlIn 2015, Slobodin and de Jong published a systematic review of family interventions for refugees . This woDisplacement can be life threatening for refugee families. Three common stages of migration  are often linked with the development of cumulative traumatic stress among forcibly displaced communities ,12 oftenMental health professionals working with refugees need to be aware that refugees encounter multiple stressors across all system levels  over prolonged periods of time  and suffAt the family subsystem, exposure to trauma and prolonged family separation during migration disrupts refugees’ family processes ,45. TrauIn couple relationships, traumatic stress affects the intimacy and marital satisfaction of trauma-affected individuals. The inability to control one’s emotional and behavioral reactions in response to traumatic memories can lead to anger outbursts ,49 and aIn parent–child relationships, trauma-affected parents may employ corporal punishment as a form of child discipline; however, they may not be able to differentiate between punishment as discipline and punishment resulting from their inability to control their anger outbursts . These rNotably, not only do family members, and particularly parents, children, and spouses, influence each other through their adverse experiences; they also influence each other through their strengths and resilience ,63,64. FAt the community subsystem level, the resources and support offered by resettlement countries define how fast refugee individuals and their families recover from adversities and cumulative traumatic stress . Local aA few notable evidence-based family interventions have been adapted for implementation with trauma-affected displaced populations. The interventions have focused on parenting ,71,72,73We followed the guidelines for systematic reviews suggested by the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) to identify the studies for inclusion in our systematic review . Our revThe initial search was conducted by collapsing all of our key terms in each database thus producing a very limited number of articles with the additional limiters of academic peer review, English language, and publication date June 2013 to February 2022. This first search generated 69 articles for screening: four from APA PsycArticles, fifteen from APA PsycInfo, five from the Psychology and Behavioral Science Collection, four from CINAHL, three from PubMed, zero from ERIC, and thirty-eight from the Social Sciences Citation Index. The hand search recommended by the second author who is the expert in the field including the six articles of the previous review by Slobodin and de Jong in 2015 resulted in eleven studies. Because the purpose of this review was to examine the effectiveness of evidence-based family interventions to address traumatic stress among refugee families worldwide, we developed inclusion and exclusion criteria. Our inclusion criteria were: (1) studies that described family-based interventions designed to address individual and relational functioning after trauma exposure and displacement, including pre and post intervention assessments; (2) studies that used Randomized Control Treatment (RCT) designs, non-experimental designs, and feasibility studies that prepared the stage for RCTs; (3) studies that involved more than one family member during the intervention ; (4) studies that targeted refugees who were affected by traumatic stress (not necessary meeting the PTSD criteria) prior to, during, and after migration; (5) studies that included refugees of all ages; and (6) studies that included interventions that were delivered both in community settings  and clinical settings .The exclusion criteria were: (1) studies that did not target the treatment of traumatic stress symptoms; (2) studies that addressed traumatic stress symptoms among refugee children and youth, but did not involve their caregivers  during the treatment; and (3) studies that did not examine the effectiveness of a relational intervention, did not address traumatic stress symptoms, or did not intervene at a family level.The initial search resulted in eighty articles, but only twelve met the full inclusion criteria, including the six articles from the previous systematic review conducted by Slobodin and de Jong (2015). Four studies were identified as being in the feasibility testing phase and included assessments and/or qualitative interviews at both baseline and post intervention stages ,72,74,77Three studies used parenting interventions ,72,73. TThe second study used a brief parenting intervention program called Strong Family with the purpose of improving mothers’ parenting skills, mothers’ perceptions of their children, and child behavior . Strong The third study used a parenting and family skills training intervention called Happy Families, adapted from the Strengthening Families Program . The intFour studies employed multifamily group interventions ,75,76,77A recent study conducted by Betancourt et al. used a home visiting intervention, the Family Strengthening Intervention for Refugees (FSI-R), that included ten 90-min weekly home-visit sessions among 40 Somali Bantu and 40 Bhutanese refugee families . SimilarAnother recent study conducted by Gotseva-Balgaranova et al. utilized an Evidence-Based Trauma Stabilization (EBTS) that included five psychodrama sessions with children and parent dyads , and four psychoeducation sessions for parents about traumatic stress symptoms and their impact on child development . This stFive studies employed school-based interventions that included caregiver group sessions prior to or/and along with children and youth’s individual and group sessions ,80,81,82The second study used International Family, Adult, and Child Enhancement Services (FACES) that included mental health assessment, therapy , psychiatric services, and support services  . FACES wThe third study used the Trauma Healing Club (THC) intervention adapted from the evidence-based Cognitive Behavioral Interventions for Trauma in Schools (CBITS) . The THCThe fourth study used the Mental Health for Immigrants Program (MHIP) that included eight CBT group sessions for children and youth, two multifamily group sessions for parents along with a child-based intervention, and training about the symptoms and effects of trauma on immigrant children for classroom teachers . The stuThe fifth study used a multimodal program that included psychoeducation for parents, creative techniques , and relaxation techniques in individual, family and group sessions . The proWe would also like to note an important study that did not meet our inclusion criteria but is directly related to our systematic review. Erdemir conducted a Preschool Education Program (PEP) with the aim of promoting holistic development and boosting school readiness skills before starting primary school among Syrian refugee children resettled in Turkey . The proNot all interventions in this systematic review measured the same outcomes of PTSD among refugee populations. Six studies reported a reduction in PTSD symptoms  in children/youth and caregivers at post intervention ,80,81,82Cultural adaptation is referred to as a process that enhances the fit between the intervention and the target population through the modification, tailoring, and adaptation of intervention elements, while still following the guidelines that contribute to the fidelity and effectiveness of the intervention . There aThe second study was conducted by Elswick et al. , and it Seven studies included in this review did not reference any specific model for the cultural adaptation of the intervention, but focused on different aspects of the relevant culture such as the use of cultural brokers, interpreters, and facilitators who were bilingual and members of immigrant populations ,77,78,82Three studies did not employ any specific model for the cultural adaptation of the intervention, nor any aspects of the relevant culture. The studies focused on the involvement of parents and teachers in addition to group sessions among students , the invImplementation science is referred to as methods that help to ensure the accurate translation of research findings and evidence-based practices into community settings with the purpose of improving the quality of health care  . In otheAll twelve studies in this review incorporated most of the main components of implementation described by Fixen et al. . SpecifiIn the third study, by Puffer et al., the intervention team consisted of 40 lay facilitators including staff and non-staff from the implementation organization . The facIn the fourth and fifth studies, led by Weine et al., the intervention team were members of the immigrant populations ,77. TheyTwo studies included preservice and in-service training, but did not say much about on-going supervision and performance evaluation during the process of intervention implementation. For example, Beehler et al. developed an intervention team that relied on refugee resettlement staff who were trained in mental health treatments, and CATS clinicians who were bilingual and trained by intervention developers . In the Three studies did not include the delivery of preservice and in-service training among their interventionist team. For example, in the study conducted by Elswick et al., the intervention team was composed of researchers and a clinician, and no other details were provided . In the This systematic review identified twelve studies that tested the effectiveness of evidence-based family interventions for displaced and trauma-affected refugees. Three studies examined parenting interventions; four studies examined multifamily group interventions; and five studies examined school-based interventions. In terms of study design, only three studies were RCTs and the remaining were non-experimental . No studies compared the effectiveness between individual-based and family-based treatments. Most importantly, only two studies specified the cultural adaptation frameworks they employed while the others simply referred to incorporating exploratory qualitative interviews, cultural brokers, bilingual research teams, and interpreters. The lack of elaboration on cultural adaptation/tailoring and specific dissemination and implementation approaches to target displaced populations points to a shortage of scientifically rigorous and culturally responsive research designs to support individual and relational health among displaced refugees of an ethnic minority.We would like to frame this discussion as a call to action for those in the mental health field regarding the serious dearth of relational interventions designed and tested to promote healing among refugees following trauma exposure and displacement. It is concerning that despite the knowledge that we face alarming and growing rates of global displacement, we as a prevention and intervention field have not ethically and responsively addressed the mental and relational health of refugee communities. In 2010, the National Institutes of Health assembled a panel to conduct an extensive Delphi study to identify grand challenges in global mental health .The report advanced the following goals: (a) identify root causes, risks, and protective factors; (b) advance the prevention and implementation of early interventions; (c) improve treatments and expand access to care; (d) raise awareness of the global burden; (e) build human resource capacity; and (f) transform health systems and policy responses. Effectively meeting these goals would simultaneously expedite the mental health treatment of refugee communities around the globe. Over a decade has passed since that report was released, yet sustained evidence of growth across those targeted goal areas is missing. We continue to struggle to address the needs of one of the most vulnerable segments of the global population, forcibly displaced refugees.Design challenges have been pervasive in developing and testing both individual and relational treatments within displaced communities. Gold standards inherent in RCT designs , also create challenges to the effectiveness and superiority of trials and often slow behavioral-based translational sciences. There have been increasing calls to expand our conceptualization of the scientific process to encompass more critical and ethically informed frames that also include deep collaboration with members of the targeted communities . For exaAnother barrier is the poor resettlement infrastructure in host countries. Considering that the largest percentages of displaced people (86 percent of refugees worldwide) resettled into middle- and low-income countries, limited and often inadequate public and mental health institutions are available to support the resettlement process . SimilarIn addition to the need for greater emphasis on both evidence-based and practice-based interventions for resettled refugee communities , an emphasis on institutional programs that enhance professional capacity, the trauma-focused training of health providers, and community-based refugee centers would go a long way in promoting successful adjustment . SlobodiMost studies in traumatic stress treatment have primarily focused on symptom reduction rather than other aspects of human relationships, such as parent–child relationships, couple relationships, sibling relationships, and both familial and community relationships. Specifically, trauma-affected refugees experience complicated grief and other comorbidities related to mental and relational issues , so we aAll studies included in this systematic review report that their programs are somewhat effective in either improving family functioning or reducing PTSD symptoms by comparing the intervention groups and control group or by comparing the pre and post test of the intervention groups. Findings also highlight that culturally adapted, evidence-based family interventions are needed for specific ethnic minority refugee populations to provide multi-systemic support after resettlement. These treatments should also incorporate the specific refugee histories of displacement, traumatic experiences, cultural values, and ethnic identities as part of a broader culturally responsive agenda for resettlement.


Three models , long-short term memory (LSTM), and LSTM with attention mechanism (LSTM-ATT)), and two modeling scenarios  were set to implement 1- to 4-week ahead predictions. A total of 24 models were built, and the results showed in general that LSTM and LSTM-ATT models outperformed RF models; modeling could benefit from using historical dengue cases as one of the predictors, and it makes the predicted curve fluctuation more stable compared with that only using climate and environmental factors; attention mechanism could further improve the performance of LSTM models. This study provides implications for future dengue risk prediction in terms of the effectiveness of GEE-based big geospatial data processing for risk predictor generation and Google Colab-based risk modeling and presents the benefits of using historical dengue data as one of the input features and the attention mechanism for LSTM modeling.Efficient and accurate dengue risk prediction is an important basis for dengue prevention and control, which faces challenges, such as downloading and processing multi-source data to generate risk predictors and consuming significant time and computational resources to train and validate models locally. In this context, this study proposed a framework for dengue risk prediction by integrating big geospatial data cloud computing based on Google Earth Engine (GEE) platform and artificial intelligence modeling on the Google Colab platform. It enables defining the epidemiological calendar, delineating the predominant area of dengue transmission in cities, generating the data of risk predictors, and defining multi-date ahead prediction scenarios. We implemented the experiments based on weekly dengue cases during 2013–2020 in the Federal District and Fortaleza, Brazil to evaluate the performance of the proposed framework. Four predictors were considered, including total rainfall (R Dengue fever, one of the mosquito-borne diseases, is mainly distributed in tropical and subtropical urban and semi-urban areas worldwide ,2. MosquAedes mosquitoes, as well as the virus incubation period, thereby affecting the spatio-temporal patterns of dengue epidemics [Different climate factors derived from weather stations, such as precipitation, temperature, and relative humidity, have been used in urban dengue risk prediction as they affect the life cycles, survival rates, and biting rates of pidemics ,6,7,8. Hpidemics . In addipidemics ,11,12, hpidemics ; howeverThe advancement of geospatial data cloud computing platforms facilitates the identification of dengue risk predictors. For example, Google Earth Engine (GEE)  hosts multi-petabyte satellite images  and global scale ready-to-use datasets on different topics , provides different algorithms for image preprocessing, spatial and temporal analysis, and image classification, and supports parallel computation ,15,16,17Diverse artificial intelligence approaches have been used in dengue risk prediction that include, but are not limited to, generalized additive model (GAM) ,19,20, rCloud deep learning is a good choice to accelerate model training with distributed hardware, and dengue risk prediction can benefit greatly from computing resources. For example, Google Colab , a free web platform with free resources of the Google servers, provides a serverless Jupyter notebook for interactive development, supports deep learning frameworks, and enables model training and evaluation using Tensorflow ,30. To dIn this context, this study aims to propose a framework by integrating GEE and Google Colab for efficient and accurate city-level dengue risk prediction. Specifically, it expects to effectively generate the dengue risk predictors using big geospatial data cloud computing based on the GEE platform, and accurately predict dengue risk based on different artificial intelligence models on Google Colab. This study expects to show the potential of integrating GEE and Google Colab in public health.A framework of efficient and accurate city-level dengue risk prediction is proposed, which includes two parts . Part  is considered as the distance of the buffer zone [Aedes mosquitoes prefer to live near people; (4) combining the time series of risk predictors and dengue epidemiological variable to generate a dataset in CSV format that is saved in Google Drive (Part (a) includes: (1) defining the epidemiological weeks according to an epidemiological calendar and generating the dengue-dependent factor by counting the number of dengue cases per week and naturally log-transformed weekly dengue case plus 1 to obtain a more stationary dependent factor; (2) considering epidemiological week and generating a suite of weekly image composites by stacking sub-daily or daily images between the beginning date and the end date of epidemiological weeks and computing a value per pixel using an algorithm ; (3) delineating the main area of dengue transmission by creating a buffer zone of 1 km around the impervious surface area and generating a time series for each predictor by spatially aggregating the values of pixels covering the buffer zone. Here, the maximum flight range of dengue vectors  includes (1) defining multi-week ahead prediction scenarios by considering time lags in advance of dengue epidemics ; (2) constructing different artificial intelligence models by taking historical dengue time series as one of the input features or not; (3) training models and evaluating the performance of models by computing evaluation indices and model comparison b.Two deep learning models  were considered in this study to predict the dengue risk. In addition, we used a classical machine learning model  as a baseline model to provide a comparison for understanding the performance of LSTM and LSTM-ATT.The conventional recurrent neural network (RNN) is susceptible to gradient vanishing and gradient explosion and is incapable of learning nonlinear relationships from long sequences . LSTM isormation :(1)ft=σWThe input gate , multiple decision trees can be integrated and are then combined to create a predictor based on the mean of each voting result from each decision tree . RF is hRMSE) and mean absolute error (MAE) as follows [The model accuracy is evaluated based on the predicted and observed weekly dengue cases by computing the root mean squared error ( follows : (11)RMSIn this study, four prediction scenarios  with two groups of input features  were used.To evaluate the performance of the proposed framework, we selected the Federal District of Brazil and Fortaleza as study sites. The Federal District of Brazil is the smallest federative unit without municipalities, and intense urban land expansion and population growth make dengue a major public health issue in recent years ,41,42. MWe obtained the dengue cases data for the Federal District and Fortaleza from a publicly available dataset, namely Arboviral disease record data-Dengue and Chikungunya, Brazil , which cWe used the impervious map of 2017 derived from the annual maps of global artificial impervious area (GAIA), a dataset containing the annual change in global impervious surface during 1985–2018 with 30 m spatial resolution . We thensum), mean temperature (Tmean), mean relative humidity (RHmean), and mean normalized difference vegetation index (NDVImean). To generate the time series with a weekly temporal resolution for each factor during 2013–2020, we first selected the data covering the predominant area of dengue transmission  [mean was derived from MODIS MOD09GA dataset [Four climate and environmental factors were used as risk predictors in this study, including total rainfall (Rsmission  with daiDAS-2.1) , and NDV dataset . Then, asum, Tmean, RHmean, NDVImean, and naturally log-transformed weekly dengue cases were combined to generate the dataset for model training and evaluation. Datasets of the Federal District and Fortaleza were split respectively, taking data in the first 326 weeks for model training and the rest for model evaluation. To fully compare the performance of RF, LSTM, and LSTM-ATT, we considered time lags in advance of dengue epidemics and defined 1- to 4-week ahead prediction scenarios with two groups of input features  and temporal scales  . AdditioWe found that LSTM-ATT modeling with historical time series of weekly dengue cases and other driving factors frequently outperformed RF and LSTM models. Previous studies also confirmed that historical dengue data of a specific epidemic area provide the temporal characteristics of dengue transmission and help to improve the prediction accuracy of future dengue cases for the area and its neighboring areas ,49,50. IThe experiments in the Federal District and Fortaleza in Brazil confirmed the feasibility of the proposed framework to a certain extent; however, there are some limitations while using the proposed framework in real-world applications. For example, many climate and environmental factors could be considered in the dengue risk prediction based on the GEE platform; however, other important factors could not be quantified, such as the cycle of dengue genotype, population mobility, mosquito population, and immune status ,52,53,54This study highlights the potential of the GEE platform and Google Colab in dengue risk prediction and also shows the benefits of using historical dengue data as one of the input features and attention mechanisms while LSTM modeling, which has important implications for future dengue risk prediction in terms of improving the effectiveness and accuracy of future dengue risk prediction. Early and accurate information on dengue transmission risk can guide the decision-making for dengue prevention and control and allow more time for the implementation of strategies.Climate change, urbanization, and population growth highlight the importance of efficient and accurate dengue risk prediction. Multi-source data downloading and processing to identify dengue risk predictors and significant time and computational resources consumed to develop deep learning models locally make dengue risk prediction a challenge. This study used GEE-based geospatial data cloud computing to effectively generate the time series of climate and environmental factors and proposed accurate forecasting of weekly dengue cases based on LSTM modeling and an attention mechanism using Google Colab, considering total precipitation, mean temperature, mean relative humidity, mean NDVI, and historical dengue cases as input features. Our findings show the great potential of GEE-based geospatial data analysis and Google Colab-based deep learning modeling for facilitating dengue risk prediction for broader use in public health.


A primary goal of precision medicine is to identify patient subgroups and infer their underlying disease processes with the aim of designing targeted interventions. Although several studies have identified patient subgroups, there is a considerable gap between the identification of patient subgroups and their modeling and interpretation for clinical applications.visual analytical modeling to automatically identify patient subgroups and their co-occurring comorbidities and determine their statistical significance and clinical interpretability; classification modeling to classify patients into subgroups and measure its accuracy; and prediction modeling to predict a patient’s risk of an adverse outcome and compare its accuracy with and without patient subgroup information.This study aimed to develop and evaluate a novel analytical framework for modeling and interpreting patient subgroups (MIPS) using a 3-step modeling approach: The MIPS framework was developed using bipartite networks to identify patient subgroups based on frequently co-occurring high-risk comorbidities, multinomial logistic regression to classify patients into subgroups, and hierarchical logistic regression to predict the risk of an adverse outcome using subgroup membership compared with standard logistic regression without subgroup membership. The MIPS framework was evaluated for 3 hospital readmission conditions: chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), and total hip arthroplasty/total knee arthroplasty (THA/TKA) . For each condition, we extracted cases defined as patients readmitted within 30 days of hospital discharge. Controls were defined as patients not readmitted within 90 days of discharge, matched by age, sex, race, and Medicaid eligibility.Q=0.17, 0.17, 0.31; P<.001, <.001, <.05), significantly replicated , and clinically meaningful to clinicians. In each condition, the classification model had high accuracy in classifying patients into subgroups . In 2 conditions (COPD and THA/TKA), the hierarchical prediction model had a small but statistically significant improvement in discriminating between readmitted and not readmitted patients as measured by net reclassification improvement  but not as measured by the C-statistic or integrated discrimination improvement.In each condition, the visual analytical model identified patient subgroups that were statistically significant (Although the visual analytical models identified statistically and clinically significant patient subgroups, the results pinpoint the need to analyze subgroups at different levels of granularity for improving the interpretability of intra- and intercluster associations. The high accuracy of the classification models reflects the strong separation of patient subgroups, despite the size and density of the data sets. Finally, the small improvement in predictive accuracy suggests that comorbidities alone were not strong predictors of hospital readmission, and the need for more sophisticated subgroup modeling methods. Such advances could improve the interpretability and predictive accuracy of patient subgroup models for reducing the risk of hospital readmission, and beyond. A wide range of studies -9 on topHowever, there is a considerable gap between the identification of patient subgroups and their modeling and interpretation for clinical applications. To bridge this gap, we developed and evaluated a novel analytical framework called modeling and interpreting patient subgroups (MIPS) using a 3-step modeling approach: (1) identification of patient subgroups, their frequently co-occurring characteristics, and their risk of adverse outcomes; (2) classification of a new patient into one or more subgroups; and (3) prediction of an adverse outcome for a new patient informed by subgroup membership. We evaluated MIPS on 3 data sets related to hospital readmission, which helped pinpoint the strengths and limitations of MIPS. Furthermore, the results provided implications for improving the interpretability of patient subgroups in large and dense data sets, and for the design of clinical decision support systems to prevent adverse outcomes such as hospital readmissions.subtypes or phenotypes depending on the condition and variables analyzed).Patients have been divided into subgroups using (1) investigator-selected variables such as race for developing hierarchical regression models  or assigcombinatorial approaches ; (4) rank the subgroups and their respective interventions using IS; and (5) use the ranking to display in descending order, the subgroup comorbidity profiles along with their respective potential mechanisms, recommended treatments, and the respective IS. Such model-based information, displayed through a user-friendly interface, could enable a clinician to rapidly scan the ranked list to (1) determine why a specific patient profile fits into one or more subgroups, (2) review the potential mechanisms and interventions ranked by their importance, and (3) use the combined information to design a treatment that is customized for the real-world context of the patient. Consequently, such a clinical decision-support system could not only provide a quantitative ranking of membership to different subgroups and the IS for the associated interventions, but also enable the clinician to understand the rationale underlying those recommendations, making the system interpretable and explainable. Our current work explores a framework called Model-based Subtype and Treatment Recommendations (MASTR) for developing such clinical decision-support systems, and evaluating them to determine their clinical efficacy in comparison to standard-of-care.The outputs from these models could be integrated into a clinical decision support system to provide recommendations for a specific patient using the following algorithm: (1) use the classifier to generate the MP of a new patient belonging to each subgroup; (2) use the predictive model to calculate the risk (R) of that patient in each subgroup; (3) generate an importance score (IS) for each subgroup, such as by calculating a thin out such dense networks such as by dropping or bundling nodes and edges based on user-defined criteria, to improve visual interpretation. However, such filtering could bias the results or modify the clusters resulting from reduced data.Although the visual analytical model enabled clinicians to interpret the patient subgroups, they were unable to interpret the associations within and between the subgroups because of the large number of nodes in each bicluster and the dense edges between them. Several network filtering methods ,57 have An alternate approach that preserves the full data set leverages the notion of analytic granularity, in which the data are progressively analyzed at different levels. For example, we have analyzed patients with COVID-19  at the cmodular with explicit inputs and outputs, enabling the use of other methods in each of the 3 modeling steps. For example, the framework can use other biclustering  [extensible, enabling elaboration of the methods at each modeling step to improve the analysis and interpretation of subgroups. For example, as discussed earlier, analytical granularity at the cohort, subgroup, and patient levels could improve the interpretability of subgroups in large and dense data sets. Third, the framework is integrative as it systematically combines the strengths of machine learning and statistical and precision medicine approaches. For example, visual analytical modeling leverages search algorithms to discover co-occurrence in large data sets, classification and prediction modeling leverages probability theory to measure the risk of co-occurrence patterns, and clinicians leverage medical knowledge and human cognition to interpret patterns of co-occurrence and risk for designing precision medicine interventions. Therefore, the integration of these different models with a focus on their clinical interpretation operationalizes team-centered informatics [Although we have demonstrated the application of the MIPS framework across multiple readmission conditions, its architecture has 3 properties that should enable its generalizability across other medical conditions. First, as shown in ization) , classifization) , and preization) . Second,ormatics  designedormatics  to work Although several studies have identified patient subgroups in different health conditions, there is a considerable gap between the identification of subgroups and their modeling and interpretation for clinical applications. Here, we developed MIPS, a novel analytical framework to bridge this gap, using a 3-step modeling approach. A visual analytical method automatically identified statistically significant and replicated patient subgroups and their frequently co-occurring comorbidities, which were clinically significant. Next, a multinomial logistic regression classifier was highly accurate in correctly classifying patients into subgroups identified by the visual analytical model. Finally, despite using a simple hierarchical logistic regression model to incorporate subgroup information, the predictive models showed a statistically significant improvement in discriminating between readmitted and not readmitted patients in 2 of the 3 readmission conditions, and additional analysis pinpointed for which patient subgroups the current CMS model might be underperforming. Furthermore, the integration of the 3 models helped to (1) elucidate the data input and output dependencies among the models, enabling clinicians to interpret the patient subgroups, reason about mechanisms precipitating hospital readmission, and design targeted interventions and (2) provide a generalizable framework for the development of future clinical decision support systems that integrate outputs from each of the 3 modeling approaches.However, the evaluation of MIPS across the 3 readmission index conditions also helped to identify the limitations of each modeling method, and of the data. The visual analytical model was too dense to enable clinicians to interpret the associations within and between subgroups, and the absence of a closed-form equation to measure modularity variance required a computationally expensive process to measure the significance of the biclustering. Furthermore, the small improvement in predictive accuracy suggested that comorbidities alone were insufficient for accurately predicting hospital readmission.By leveraging the modular and extensible nature of the MIPS framework, future research should address these limitations by developing more powerful algorithms that analyze subgroups at different levels of granularity to improve the interpretability of intra- and intercluster associations and the evaluation of subgroup-specific models to predict outcomes. Furthermore, data from electronic health records made available through national-level data initiatives, such as National COVID Cohort Collaborative and TriNetX, now provide access to critical variables, including laboratory results and comorbidity severity, which should lead to higher accuracy in predicting adverse outcomes. Finally, extensive discussions with clinicians have confirmed the need for decision support systems that integrate outputs from the 3 models to provide for a specific patient, predicted subgroup memberships, and ranked interventions, along with associated subgroup profiles and mechanisms. Such interpretable and explainable systems could enable clinicians to use patient subgroup information for informing the design of precision medicine interventions, with the goal of reducing adverse outcomes such as unplanned hospital readmissions and beyond.