This study aims to improve the effectiveness of English grammar teaching in high school. Firstly, the Science, Technology, Engineering, Art, and Mathematics (STEAM) educational model is comprehensively discussed. Then, the current situation and difficulties of English grammar teaching in high school are analyzed. Finally, based on the traditional and the STEAM teaching mode, a comprehensive study on the psychological motivation of students is carried out in high school English grammar teaching. The traditional teaching model had little effect on students’ psychological motivation in the process of English grammar teaching. Therefore, students’ satisfaction with English grammar teaching under the traditional teaching model is very low, especially in innovation and practice. The STEAM teaching model has greatly improved the psychological motivation of high school students in the English grammar teaching process. The overall satisfaction rate of the students with the English grammar teaching in the STEAM teaching model is 70%. This teaching model can greatly improve students’ requirements for innovation and practice. This study not only provides a reference for the improvement of English teaching in high schools but also contributes to the reform and development of education. Education is an eternal development project of society because the development of education can drive the development of the whole society. Therefore, human research on education is very necessary . EnglishTo sum up, firstly, the STEAM education model is discussed. Secondly, the present situation and connotation of English grammar teaching in senior high school are explored. Finally, the traditional high school English grammar teaching and the English grammar teaching effect under STEAM education are compared, and the influence of the STEAM education model on the psychological mechanism of high school students is studied. The innovation lies in introducing the motivational factors in psychology and comprehensive analysis of students’ English learning according to different classifications. This study provides a reference for improving the effect of English grammar teaching in high school and contributes to improving the quality of social education.Science, Technology, Engineering, Mathematics (STEM) education originated in the United States. It is a teaching model with links to science, technology, engineering, and mathematics . HoweverScience, Technology, Engineering, Art, and Mathematics education also structures science, technology, engineering, mathematics, and the arts to a certain extent. This arrangement is arranged in the spatial structure and plays a role in people’s world transformation and knowledge analysis . In STEAIn English grammar teaching in high school is a very important part of English teaching. English teaching in China starts from primary school. The general English teaching content in elementary school is simple word spelling and sentence reading. The content of English teaching in junior high school is generally word memory, sentence reading and writing, and simple grammar teaching. In high school, students need to be taught in-depth grammar and difficult words. Therefore, it is very important to study English teaching in high school. The traditional high school English grammar teaching is mainly through the teacher’s explanation and teaching. Students learn by rote memorization and grammar in sentences . This teAccording to the current knowledge of English grammar teaching, the problems of English grammar teaching in high school are lack of interest, lack of flexibility, lack of practicality, and lack of creativity. The lack of interest is caused by the way teachers teach in the teaching process. Psychologists believe that interest in learning is the main driving force that drives a person to learn and perceive the world . TherefoThrough these analyses, the current situation of English teaching in senior high schools lacks innovation, skills, and practical teaching. The STEAM teaching model is used to improve English grammar teaching in high school, which can greatly enhance its teaching method and students’ interest in learning . The higIn The psychological motivation of high school students is more complex. High school students have deepened their cognition of the outside world, which leads to their complex psychology. They have more subjects and heavier learning tasks. The combined effect of these two aspects has deepened the psychological pressure on students . In the Age difference means that students will have different psychological changes with age in the learning process. Gender difference refers to the different learning psychology between boys and girls in high school. Individual differences mainly include differences in students’ abilities, temperaments, characters, and interests. The influencing factors of learning psychology mainly include learning motivation, personality, and the social environment in which students live . LearninThis study uses a questionnaire survey to conduct psychological research on high school students. It explores the influencing factors of the application of the STEAM education model to high school English grammar teaching. In the process of conducting the questionnaire survey, this study mainly conducts research on the students of a middle school. Firstly, it classifies the students by gender, study subject type, and grade. Secondly, through multi-angle research, students’ learning conditions are comprehensively evaluated, reflecting the specific role of STEAM teaching methods on students. Finally, the students’ satisfaction with the teaching model is evaluated by analyzing the research results. The basic information of the research object is shown in In This study makes statistics on the questionnaire data received and analyzes it with the Analysis of Variance (ANOVA) method. By comparing students’ educational results before and after, the specific changes of middle school students in the STEAM education environment are determined to analyze the role of the STEAM education model.Firstly, students are surveyed on English grammar. The specific reactions of traditional English grammar teaching methods, teaching contents and teaching effects are analyzed through the investigation and study of students’ overall psychological performance of English teaching. Thus, the rationality of the grouping is verified. The specific psychological mechanism of students on traditional English grammar is shown in In The STEAM education model is used to conduct research on English grammar teaching in high schools. The teaching mode plays a great role in innovation, practice, and comprehensive application. Therefore, the research can provide important reference for English grammar teaching in high school, and provide a certain impetus for the improvement of traditional teaching mode. In In This study aims to improve the comprehensive effect of applied grammar teaching and promote the development of English grammar teaching in senior high schools. The STEAM teaching model is used to analyze the English teaching effect on high school students to improve the students’ learning situation and promote the reform of the teaching mode through research. Firstly, through different classifications, students are surveyed on traditional teaching models. In the three categories, different students showed great differences. Among different gender categories, girls are more satisfied with traditional teaching methods. Girls have the highest percentage of satisfaction with the learning environment and the lowest percentage of satisfaction with teaching practice; boys have the highest percentage of satisfaction with the difficulty of teaching content and the lowest percentage of satisfaction with teaching practice. Students of different genders are less satisfied with teaching innovation and teaching practice. Both liberal arts and science students are more satisfied with the difficulty of teaching; art students are more satisfied with the teaching method. Students are the least satisfied with both teaching innovation and teaching practice. Across the grades, students are the most satisfied with the learning environment; the least satisfied with teaching innovation and teaching practice. Students’ satisfaction with the teaching innovation and teaching practice of traditional teaching is relatively low. Secondly, in investigating students’ learning status under the STEAM education model through different classifications, the gap between students is reduced. STEAM has a great influence on the psychological mechanism of high school students and can promote the overall development of students. Compared with the traditional teaching mode, after the application of STEAM teaching, students’ satisfaction with teaching innovation and teaching practice has been significantly improved. Finally, the learning status of high school students under the traditional and the STEAM teaching mode is compared, and the students’ learning status has changed significantly under the STEAM teaching mode. In the current educational environment, the STEAM teaching model can effectively promote the development of teaching reform. Students are investigated more directly than This study studies the psychological mechanism of students in the process of high school English grammar teaching based on the STEAM education model and analyzes the students’ satisfaction with the teaching process between the traditional research model and the STEAM education model. Firstly, English grammar teaching in senior high school is comprehensively discussed. Then, the STEAM education model is analyzed. Finally, based on the STEAM English grammar teaching model, the comprehensive satisfaction of students is investigated. The results show that under the traditional teaching model, students’ overall satisfaction with the teaching process is low, and students’ overall satisfaction with teaching innovation and teaching practice is the lowest. This study surveyed students after adopting the STEAM education model, and the students’ satisfaction with all aspects of the teaching process has been greatly improved. Students’ overall satisfaction is relatively high, and the overall satisfaction rate is above 70%. Students’ satisfaction with teaching innovation and teaching practice has been improved the most, and high school English grammar teaching has high requirements in terms of innovation and practice. Therefore, the STEAM teaching mode plays an important role in improving students’ learning psychology. STEAM teaching mode can significantly improve students’ learning situations. This study provides the influencing factors of STEAM teaching mode on the psychological mechanism of high school students, the specific factors provided are less involved. Therefore, future research will increase the influencing factors of psychological mechanisms to improve the STEAM teaching model further.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by De La Salle University Ethics Committee. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.The author confirms being the sole contributor of this work and has approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


There is a large proliferation of complex data-driven artificial intelligence (AI) applications in many aspects of our daily lives, but their implementation in healthcare is still limited. This scoping review takes a theoretical approach to examine the barriers and facilitators based on empirical data from existing implementations. We searched the major databases of relevant scientific publications for articles related to AI in clinical settings, published between 2015 and 2021. Based on the theoretical constructs of the Consolidated Framework for Implementation Research (CFIR), we used a deductive, followed by an inductive, approach to extract facilitators and barriers. After screening 2784 studies, 19 studies were included in this review. Most of the cited facilitators were related to engagement with and management of the implementation process, while the most cited barriers dealt with the intervention’s generalizability and interoperability with existing systems, as well as the inner settings’ data quality and availability. We noted per-study imbalances related to the reporting of the theoretic domains. Our findings suggest a greater need for implementation science expertise in AI implementation projects, to improve both the implementation process and the quality of scientific reporting. Nowadays, artificial intelligence (AI) has become ubiquitous, and much more advanced and user-friendly than it was two decades ago. In many respects, AI has become reliable and permeates many aspects of our daily lives, such as face and speech recognition apps. Yet, only recently have we seen a corresponding rate of adoption in the healthcare services. AI systems can emerge as a smart solution to reduce clinical staff workload in a world with increasingly saturated healthcare systems. AI is different from simple technology interventions in the sense that AI does not just manage data, but it provides suggestions and recommendations directly shaping the clinical decision process ,2.There exists a wide body of literature on the barriers to and facilitators of implementing AI in healthcare ,4,5. HowImplementation science is a fairly new field, whose emerging theories, models and frameworks have the potential to inform our understanding of AI implementation in a more widely accessible and systematic way. This multidisciplinary approach, combining AI and implementation science, transcends the traditional boundaries of each of the fields. Blending these two disparate, yet complementary, fields is key to our understanding of AI implementation in healthcare. However, there is a need to reconcile the methodological differences and conflicting domain-specific jargon. In the next two subsections, we explore the fundamental aspects of each of these two fields.AI is not a new concept, but renewed interest in the field is widely attributed to the increasing abundance of digital data and the advancements in data analytic approaches. AI comprises many different areas that range from logic-based models to machine learning (ML). Logic-based models  have beeIn the scope of this study, we refer to AI as systems that are used to solve healthcare problems of interest and are powered by ML. Witten et al.  define MIn a seminal paper, Eccles and Mittman  define iIn the context of screening for implementation trials, there is a thin line between a pilot study and an implementation trial. Pilot studies and feasibility studies are necessary components in the path to implementation. Curran et al.  describeThe goal of this scoping review is to characterize the barriers and facilitators influencing the implementation of ML methods in the healthcare setting. This study differs from the existing reviews in at least two major ways. First, whereas the existing reports on barriers and facilitators are fragmented, this study analyzes these barriers and facilitators in a more systematic and theoretic way, which allows us to identify reporting problems and knowledge gaps. Second, the existing reviews do not discriminate based on the phases of implementation. Therefore, most of the studies include algorithm development, efficacy and effectiveness studies. The current study, on the other hand, focuses on empirical observations from the late phases of implementation and roll-out.This scoping review follows standard reporting, based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping reviews   algorithm implementation, (ii) efficacy, effectiveness or algorithm validation, (iii) implementation trial, or (iv) full implementation. An overwhelming majority of the search hits fell within (i) and (ii). Only the studies identified as class (iii) or (iv) were included in this study.The basic structure of the search query was «Artificial intelligence AND implementation AND healthcare». Synonyms and terms related to AI were then added using the logical disjunction operator (OR). The initial abstract screening was done using Rayyan . All the AND (implement* [Title] OR practice [Title] OR approved [Title]) AND (y_10[Filter]))The data extraction variables were developed through weekly brainstorming sessions. At least four co-authors  participated in each brainstorming session, defining the list of topics relevant for extraction. The initial sessions were focused on the free definition of the topics and variables useful for extraction. As the brainstorming sessions advanced, the categories of variables were inductively defined, leading to the final list of agreed variables for data extraction as shown in We used the Mixed Methods Appraisal Tool (MMAT)  to critiQualitative methods were used to synthesize the extracted facilitators and barriers based on the Consolidated Framework for Implementation Research (CFIR) see . CFIR isFive co-authors  read the full papers, extracting any section that pointed to a possible barrier or facilitator. Free comments  from the reviewers were allowed. All the papers were reviewed by at least two co-authors. Both text segments and free comments were imported into the qualitative analysis software, MaxQDA , for furTwo co-authors  went through the extracted segments of the selected papers independently. Initially, a deductive approach to code the segments into CFIR constructs was used. After one iteration, the constructs were considered not granular enough. Then, two reviewers  proceeded with an inductive approach, with no predefined code list. The reviewers marked all the segments of text that indicated a barrier or a facilitator for AI adoption.Once all the papers had been coded, the reviewers met with three other members of the team, who had read all the papers but had not coded the texts. Iterative meetings were performed to go through all the coded texts and crosscheck the results. Equivalent labels were merged into one single concept when agreements were found. Any disagreements were discussed until all the members agreed on the optimal concept to code a specific fragment of the text by checking the full text and re-reading the section of interest. The usual sources of disagreement were the scope of one concept and the specific barriers and facilitators that one concept should encompass.The same concept could be described as a barrier or as a facilitator by different studies  independently screened the titles and abstracts according to the inclusion and exclusion criteria. This resulted in the removal of 2668 articles, leaving 116 relevant articles. A full-text assessment was conducted on these 116 relevant articles, which resulted in 19 included articles, as shown in n = 9/19); about a quarter  were conducted in Northeastern Asia, and only three in Europe. Several medical fields were represented: sepsis , diabetes , cardiology , mental health , emergency care  and palliative care , and the rest were for all patients . The most common medical task (an AI-use case) was screening . Only two of the studies had AI systems targeted towards use by patients, while the rest were meant to be used by clinicians or healthcare staff . In terms of AI algorithms, the majority of the studies applied deep learning .As shown in n = 11/19), qualitative  and quantitative descriptive . In all the quantitative studies, the participants were representative of the target population. For all but one of the qualitative studies, the methods used were appropriate to answer the posed questions.In terms of the appraisal, three co-authors  used the MMAT template to independently appraise the included studies see —MMAT, ann = 27/57). None of the reviewed articles reported barriers related to management and engagement. The second most common facilitators were related to the theme of evaluation and testing , while a third were related to technical design . For the barriers, the most common were interoperability issues , data quality and management  and trust and transparency .The concepts identified through the inductive process were further classified into ten broader themes: evaluation and testing, background, management and engagement, data quality and management, trust and transparency, clinical workflow, interoperability, finance and resources, technical design and AI policy and regulation. As illustrated in n = 24/69). The second most popular facilitators were based on III. Inner setting , followed by I. Intervention characteristics . Most of the barriers were reported for I. Intervention characteristics , followed by III. Inner setting  and II. Outer setting, which had the least number of both barriers and facilitators.The mapping between the coded concepts and the corresponding CFIR constructs is available in [Viewed in total, the reporting of facilitators and barriers related to the I. Intervention characteristics, III. Inner setting and V. Process appear somewhat balanced, with some under-reporting of the II. Outer setting and the IV. Characteristics of individuals. Viewed per study, however, the reporting imbalances are more apparent, and we highlight two kinds of imbalance. The first case relates to how a study can concentrate on a single theoretic domain and neglect the rest, and the second is where, regardless of the theoretic domain, a study focuses on either one of facilitators or barriers.In the first case of theoretic domain imbalance, some studies focused on the characteristics of the intervention ,49, whilWe further describe some of the salient highlights in each of the five CFIR domains. These highlights are based on the frequency of discussion they generated in the included studies. The major facilitating factors were related to the implementation process itself and the involvement of users, and this finding is consistent with the literature . In contML algorithms need to continuously learn from new data. As data change and as methodological techniques advance, so must the models, and this presents several challenges. One of challenges is the continuous need to validate algorithms and test whether their specificity and sensitivity have deteriorated. This partially explains why many of the included studies conducted fresh validation tests.In the validation process, it is essential to make sure the training data represents the population to which the AI system is applied. In practice, results may not be representative across populations ,48, and Technical design decisions might affect the implementation by facilitating or hindering this process. Usability was cited as both an important facilitator and a barrier. For instance, Romero-Brufau et al.  faced prIn order to successfully implement an AI system in a clinical workflow, the system must interoperate with the targeted hospital systems. In , the lacA lack of integration with the clinical workflow can be a barrier to the implementation process. For example, Sendak et al.  reported“Models that require additional work, even if it is as little as looking at another screen and clicking a few more times, are much less likely to be implemented or sustained” .As a solution, some studies showed that ML-based methods compatible with logic-based CDS methods are easier to integrate in the clinical workflow. An example is the use of neural networks for knowledge discovery during the development stage, where results have been later discretized as Arden syntax ECA rules in the production stage .The outer setting was discussed by only one study , mostly The availability of high-quality data resources within the organization was discussed as an important determinant factor. Most of the studies used electronic health records (EHR) as the primary source of data, and they reported their complexity and inadequate use as a barrier ,42. Due “…key data that reliably predict the outcome of interest may not be readily available as structured, discrete data inputs from the EHR…” Missing data, noisy data, or data without proper labels and identifiers were among the main factors that lowered data quality and were consequently reported as barriers. Besides data quality, the frequency of data updates is another important issue in maintaining the validity of predictive models . AlthougThis domain speaks to the perceptions and beliefs of the individuals involved in the implementation. For instance, a lack of trust among clinicians might hinder the implementation process. The clinicians must trust that the system maintains good sensitivity and specificity and provides trustworthy suggestions in line with evidence-based practice and clinical judgement. As Sendak et al.  note:“Clinical leaders prioritized positive predictive value as a performance measure and were willing to trade-off model interpretability for performance gains”.At the beginning of the implementation process in , the phyAnother factor is explainability, a characteristic that directly conditions the transparency and trust of the AI implementation, which, in turn, are precursors of privacy and fairness . No explUser involvement ranked as the most reported facilitator, followed by the education of key stakeholders. In the very beginning of a project, it is useful to have a common justification  and an eThe barriers and facilitating factors emerging from this study are not surprising, since they are widely reported in the literature. The included studies presumably have overcome many of the barriers since the studies are based on the late stages of implementation. We expected that insight into the determinants of their successes would shed new light on our basic understanding of AI implementation in clinical settings. However, what we uncovered was insufficient and imbalanced reporting of some key theoretical domains, which suggests a lack of implementation science expertise in the reporting of relevant projects.The traditional recommendation for e-health implementation processes is to involve both ICT and clinical domain experts. All the included studies seem to have followed this basic recommendation, but our findings suggest there still is a missing piece of the puzzle-socio-organizational considerations. Considering the successes of implementation science as a field, perhaps it is time we looked beyond these traditional recommendations in order to uncover additional synergies based on new modes of inquiry native to implementation science, integrating insights from social science theories and abstractions.We also showed that domain differences between AI and implementation science have an impact on multidisciplinary research. Since implementation has become a key aspect of AI in healthcare, it is important to unify the vocabulary to make relevant research more accessible to both fields. This could start with annotating relevant publications with an appropriate keyword indicating the implementation stage or purpose of the study, for example, using Curran et al.’s  Hybrid TPerhaps one of the major limitations of this study is the uncertainty regarding coverage of the relevant literature, which was conditioned by multiple factors. First, we noted that some AI implementations might not have been subject to rigorous scientific study or evaluation, while other implementations were only reported locally in internal reports. This made the implementations essentially inaccessible. In addition, ambiguity related to terminology was a huge factor in successfully identifying all the relevant studies. We allude to the difficulties of defining implementation and the consequences it had on our search strategy and screening.It is possible that attentional bias is a factor in our findings. Since we set out to identify advanced implementations, it is conceivable these implementations faced comparatively fewer challenging barriers than those of a typical implementation. This might partially explain why there were many more facilitating factors than barriers. In looking at successful implementations, it is quite possible we missed many important barriers from failed implementations.This study exemplifies a theory-based approach to synthesizing determinants of AI implementation success and formalizes known gaps and biases related to how AI implementations are reported. In addition to highlighting the major facilitators and barriers, we noted a widespread imbalance and insufficient reporting of AI implementations in clinical settings. We single out the II. Outer setting and IV. Characteristics of individuals as two key theoretical domains, which were not fully explored in the included studies. As a result, we know very little about the knowledge and beliefs, self-efficacy and other personal attributes of the people involved in the implementations. Similarly, any policies, incentives, collaborative networks or competitive pressures that helped or hindered these implementations are largely unknown. These factors represent an important knowledge gap and require further inquiry before AI implementation in healthcare can be more fully understood.Further, we recommend two remedial actions based on our findings: (i) implementation science expertise should be a part of every AI implementation project in healthcare in order to improve both the implementation process and the quality of scientific reporting, and (ii) scientific publications involving AI implementations in clinical settings should be annotated with an implementation stage or purpose to make relevant research more easily accessible.


The high prevalence and severity of depression in adolescents with polycystic ovary syndrome (PCOS) is a critical health threat that must be taken seriously. The identification of high-risk groups for depression in adolescents with PCOS is essential to preventing its development and improving its prognosis. At present, the routine screening of depression in adolescents with PCOS is mainly performed using scales, and there is no early identification method for high-risk groups of PCOS depression in adolescents. It is necessary to use a warning model to identify high-risk groups for depression with PCOS in adolescents.Model development and validation will be conducted using a retrospective study. The study will involve normal adolescent girls as the control group and adolescent PCOS patients as the experimental group. We will collect not only general factors such as individual susceptibility factors, biological factors, and psychosocial environmental factors of depression in adolescence, but will also examine the pathological factors, illness perception factors, diagnosis and treatment factors, and symptom-related factors of PCOS, as well as the outcome of depression. LASSO will be used to fit a multivariate warning model of depression risk. Data collected between January 2022 and August 2022 will be used to develop and validate the model internally, and data collected between September 2022 and December 2022 will be used for external validation. We will use the C-statistic to measure the model's discrimination, the calibration plot to measure the model's risk prediction ability for depression, and the nomogram to visualize the model.The ability to calculate the absolute risk of depression outcomes in adolescents with PCOS would enable early and accurate predictions of depression risk among adolescents with PCOS, and provide the basis for the formulation of depression prevention and control strategies, which have important theoretical and practical implications.[ChiCTR2100050123]; Pre-results. Polycystic Ovary Syndrome (PCOS) is a female endocrine metabolic disease of unknown etiology, with a prevalence of 5.6 to 11.04% in adolescence . SymptomDepression high-risk groups are individuals or subgroups who suffer from certain depressive symptoms but do not meet the diagnostic criteria for depression . They coThe development of warning models often involves the incorporation of influencing factors. However, a variety of factors affect depression in adolescents, including: (i) biological factors, such as hormones and individual development during adolescence, which modifies neuroendocrine regulation and brain structure in diverse ways, contribute to adolescent depression. For instance, altering the activity of the hypothalamic-pituitary-adrenocortical (HPA) axis, affecting the release of neurotransmitters, altering the REM sleep latency, and so on, can all have an impact on the neurological structure and psychological state of the adolescent population, eventually leading to depression ; (ii) inAs previously noted, the occurrence of depression in adolescents with PCOS involves multiple dimensions and contains numerous independent variables. To minimize the bias caused by the absence of important independent variables, it's important to select as many variables as possible at the beginning of the warning model construction process. Traditional regression analysis methods using statistical software such as SPSS, Jamovi, etc., can quantitatively analyze the strength of a predictor based on the regression coefficient. However, conventional regression analysis methods cannot handle collinearity problems and produce high variance when there are numerous independent variables. Least Absolute Shrinkage and Selection Operator (LASSO) is a type of machine learning method introduced by Tibshiran in 1996 . On the The aims of the study are to: (i) Develop a depression prediction model for adolescents with PCOS to identify at-risk populations early; (ii) Validate the accuracy and validity of the model and to assess the clinical utility of the model.n = 2), psychiatrists (n = 2), psychologists (n = 1), nursing specialists (n = 1), statisticians (n = 1) and research assistants (n = 2). Members of the research team have received uniform training in PCOS clinical practice and adolescents as well as extensive experience in their respective fields. Firstly, the team derived an understanding of depression among adolescent PCOS patients by retrieving evidence-based information and integrating clinical practices. Secondly, the research team reviewed the factors associated with depression in adolescents in the general population, and then the pathological factors of PCOS, the perception factors of PCOS, the diagnosis and treatment factors of PCOS, and the symptoms-related factors of PCOS, to summarize the risk factors for depression in adolescents with PCOS.A multidisciplinary research team was established, consisting of obstetricians and gynecologists .According to “Chinese guidelines for diagnosis and treatment of polycystic ovary syndrome,” which was developed by the Endocrinology Group of the Chinese Medical Association Obstetrics and Gynecology Branch in 2018 , PCOS paAble to self-report .Participants and their families volunteer to participate in the study.Participants with cognitive impairment and major mental illness schizoph.Participants with serious comorbidity.Other conditions that lead to elevated androgen levels  and cause ovulation disorders .After discussion and conclusion, the research team obtained indicators of independent and dependent variables affecting depression in PCOS adolescents, as follows.Depression will be measured by the Chinese version of the Children's Depression Scale(CDI) . The scaWe will use the Kiddie-Schedule for Affective Disorders and Schizophrenia-Epidemiological version (K-SADS-E) to confirm the diagnosis of depression and exclude possible comorbidities. The validated Mandarin version will be used in this study . It is aThe predictors for building the model are shown in via a questionnaire platform and collated into Excel format, with the two researchers collecting the information independently and negotiating any disagreements.Two uniformly trained researchers will identified participants based on inclusion and exclusion criteria. Firstly, a researcher will guide participants and their parents to a quiet outpatient room that is conducive to dialogue and data collection. Another researcher will explain the purpose, significance, and implementation of the study to participants and their parents in a one-on-one format, assuring them that their privacy would be protected. With the consent of participants and their parents, researchers will sign informed consent forms with them. Afterwards, psychiatrists within the study team will conduct semi-structured interviews with participants and their parents, focusing on the diagnosis of depression and excluding possible comorbidities. The researchers will distribute the QR code of the scale that was included in the questionnaire platform in advance to participants who are eligible, and the participants will scan the QR code on WeChat to fill out the form online. Finally, the researchers will give the participants health education and prescription advice, as well as recording laboratory indicators including sex hormones and insulin. The total planned time for this process will be around 2 h. The data will be extracted The screening process will strictly adhere to the inclusion and exclusion criteria.Prior to conducting formal research, researchers will receive standardized training.Before the survey, researchers will explain the contents and precautions of the questionnaire to participants and their families. To ensure participants understand items, researchers will use uniform language to explain them, while avoiding leading words.The questionnaire must be completed without omissions to ensure completeness of content and efficient recall.Two researchers check and correct any discrepancies with the original data to ensure accuracy.In the case of a binary outcome, an approximate 95% confidence interval for the total outcome proportion (ϕ) isTherefore, the absolute margin of error (δ) is For this reason, in order to get an accurate estimate of the overall outcome probability in the target population, based on the expected outcome proportion , mean ± SD, or number/frequency (percentage). Statistics will be considered significant when the P-value < 0.05.The data obtained will be analyzed using SPSS Statistics version 18 (IBM Corp) software and evaluated using comparative and descriptive statistics. Student The collected data will first be preprocessed, including data cleansing, outlier processing, and data transformation. Data cleaning is the process of deleting redundant information and correcting information with obvious errors. Based on the data situation, we will choose between deletion, imputation, or substitution for outlier processing. The data will be transformed in the following ways: continuous variables will be kept in the model as continuous variables if they are linear or will be combined with similar segments if they are non-linear (treated as binary categories or ordered categories); ordered multicategorical variables will be treated as rank variables or dummy variables as appropriate, and when not linearly related to the outcome, optimal scale regression will be used to explore effect inflection points; unordered multicategorical variables need to be split into multiple binary variables .Preprocessed data will be assigned variable values according to various variable types and a data set in CSV format will then be generated.After the data set is imported into the R software, it will be divided into 80% training data and 20% validation data. The training data will be used to build the warning model, and the validation data will be used for internal validation.To begin with, we will use R software to read the features of the dataset. After that, we will load the two R packages “corrplot” and “glmnet.” By using the cor function, we will create a correlation object “corrlate” and visualize the correlation coefficient. The predictors and outcome variables in the training and validation sets will be encoded into matrices, the predictors will be stored in X vectors, and the outcome variables in Y vectors. A LASSO regression will be performed when alpha is set to one. The program will then be run. When the parameter lambda is greater, the estimated parameters will also be compressed to a smaller degree. As lambda reaches a certain value, some variables that are not important to the model will be compressed to zero, indicating that these variables will be eliminated. Thus, the lambda corresponding to the optimal result, the influencing factors of preliminary screening, and the regression coefficient will be obtained.Internal validation will be conducted using the 20% validation set samples and will utilize a cross-validation method based on 10 folds. Then, the result of LASSO cross-validation will be obtained, and the lambda when it is one standard error away from the mean square error will be considered optimal . For that lambda case, we will examine the regression coefficients and exclude variables with a regression coefficient of zero. Those variables with regression coefficients other than zero are selected, thereby identifying the influencing factors that are significantly associated with depression in PCOS adolescents. After screening the variables, a multivariate linear model will be constructed.The prediction accuracy of the model will be reflected by discrimination and calibration. In this study, the C-statistic is used to measure discrimination, followed by a calibration plot to measure the accuracy of the absolute risk predictions of the warning model. An evaluation of the fit will be performed by drawing a scatter plot.External validation will be conducted using temporal validation with data collected from September 2022 to December 2022, to ensure model portability and generalizability. This means that model risk scores will be calculated rigorously and the model will be refitted in the validation cohort.After the model is defined, we will consider the nomogram as the final presentation form in order to facilitate clinical application of the model and assist medical personnel in identifying high-risk groups of depression in adolescents with PCOS.All statistical analysis will be performed using R software (Windows version 4.1.2).Data will be entered and stored through a third-party electronic platform, and all documents and data will be password-protected on a network within the platform, accessible only to researchers. Data will be exported in Excel format for further analysis when processed.This research protocol aims to construct and validate a warning model for depression in adolescents with PCOS. It will answer three questions: what factors influence depression risk in adolescents with PCOS; what role do various types of depression risk factors play in the development of depression in adolescents with PCOS; and how interpretable and accurate is the LASSO-based warning system.There are many factors associated with depression in adolescents with PCOS. For the first time, a depression warning model will be constructed that takes into account not only general factors like individual susceptibility factors, biological factors, and psychosocial environmental factors of depression in adolescence, but also the pathological factors, illness perception factors, diagnosis and treatment factors, and symptom-related factors of PCOS. This study will precisely and early identify depression at-risk groups for PCOS in adolescence by including the influencing factors of several dimensions of depression into the model and leveraging LASSO's ability to automatically screen variables and boost model interpretability.So far, it has been difficult to identify adolescents at high-risk of depression, which has a severe impact on adolescent mental health. In this study, the research population consists of adolescents with PCOS, who have a significantly greater prevalence of depression and more complicated impacts than general adolescent girls, an important representative sample. Through this study, we hope to remove the bottleneck of identifying adolescents at risk of depression. The success of this study can not only identify the high-risk groups of depression in adolescents with PCOS, but also demonstrate the risk factors and key links associated with depression in adolescents with PCOS. At the same time, it can be extended and applied to the management of high-risk groups of depression throughout adolescent girls, thereby contributing to reducing the prevalence of depression in adolescent girls and implementing comprehensive and effective prevention measures.The study is a retrospective cohort study, and the questionnaire will be partially completed by patients' recollection of recent events, which may be subject to a degree of recall bias. In prospective cohort studies, depression outcomes and factors contributing to depression can be longitudinally tracked over a period of time of high-risk groups with PCOS in adolescence, which can help to make models more robust and representative. Data for this study will be collected at one hospital, so it will be a single-center study. It is proposed that future studies should collect data from multiple hospitals simultaneously to increase sample size and persuasiveness. In this study, participants will be selected in accordance with the diagnostic criteria prescribed in the “Chinese guidelines for diagnosis and treatment of polycystic ovary syndrome.” Due to the inconsistency in diagnostic criteria among different ethnic groups, the generalizability of this study is limited to some degree. It is recommended that metformin and oral contraceptives be used to treat PCOS in adolescence, in accordance with international evidence-based guidelines . It has The studies involving human participants were reviewed and approved by the Ethics Committee of ZunyiMedical University: Zunhe Lun Review [2021] 1-093. Written informed consent to participate in this study will be provided by the participant's legal guardian/next of kin. Written informed consent will be obtained from the individuals and minor's legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article.RD was involved in writing the protocol, editing the manuscript, setting up the trial, and acquisition of data. HZ, XY, YG, YL, HT, XW, and YW participated in the design of the trial, analysis, and interpretation of the data. LW carried out the conception, design, and final approval of the version to be published and agreed to be accountable for all aspects of the work by ensuring that any questions regarding the accuracy or integrity of any part of the work are appropriately investigated and resolved. All authors contributed to the article and approved the submitted version.The work is supported by Zunyi Science and Technology Planning Project [Zun Shi Ke He HZ Zi (2021) No. 21]. The Science and Technology Department of Guizhou Province, China [Grant No. Qian Ke He (2017) 5733-077]. The Health Commission of Guizhou Province, China (Grant No. Gzwjkj2019-1-018).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Innovation is considered to be a dominant cause for sustainable business success. Knowledge management and intellectual capital are powerful tools to promote innovation in the organization. Therefore, this study aims to explore the influence of knowledge management process and intellectual capital on innovation with the mediating effect of entrepreneurial orientation and moderating role of leader education level. Data were collected from a sample of 393 IT firms listed in the Pakistan Software Houses Association and applied a partial least squares structural educational modeling (SEM) technique. The results show that the knowledge management process and intellectual capital have a positive effect on innovation. Moreover, the study confirms that entrepreneurial orientation partially mediates the relationship between knowledge management and intellectual capital on innovation. Furthermore, the moderation effect of a leader’s education was confirmed for the knowledge management-innovation relationship whereas, the moderation effect of the leader’s education on intellectual capital and innovation relationship was insignificant. Recommendations for practitioners and future research directions were also discussed. Globalization and growing technological changes have forced organizations to adopt more creative and innovative work patterns . IndividIntellectual capital is considered the best powerful competitive weapon and a very precious asset of an individual . IntelleEntrepreneurial orientation is a vital source for the success of an organization. Entrepreneurial orientation is extracted into entrepreneurship; it takes all action, adopted techniques, and decision-making activities that encourage starting a new business . MoreoveAccording to The literature-based evidence has been spotted where the relationship between grassroots innovation and entrepreneurial success was fully mediated by entrepreneurial orientation . So, in This study contributes to the literature on knowledge management, intellectual capital, and innovation because this is the first study that links knowledge management and intellectual capital with innovation through the mediating effect of entrepreneurial orientation and the moderating role of a leader’s education in the IT (hi-Tec) sector of Pakistan. After all, prior studies explore different outcomes of intellectual capital and innovation , while lBased on the above-mentioned research inputs, this study aims to identify these research gaps in the conceptualization of knowledge management process on entrepreneurial orientation and innovation with the moderating role of leaders’ education. Therefore, within the developed research model, the study investigates the following research objectives:RO1: To identify the impact of knowledge management process and intellectual capital on entrepreneurial orientation and innovation.RO2: To assess the mediating effect of entrepreneurial orientation in the relationship between knowledge management process and intellectual capital on innovation.RO3: To examine the moderating role of leader education in the relationship between knowledge management process and intellectual capital on entrepreneurial orientation.The remaining part of the article is ordered as follows: Section “Theoretical Framework” discusses the theory and hypotheses of development. The methodology and data are presented in section “Materials and Methods,” while empirical results are reported in section “Results,” and discussion and conclusion are explained in section “Discussion and Conclusion.” Theoretical and practical implications are explained in section “Implications.”The foundation of the current study was established on entrepreneurial orientation and the resource-based view (RBV). This explains how entrepreneurial efforts and organizational internal resources should be focused on because these are the center of attention in the RBV theory and concepts of entrepreneurial orientation . The RBVPrior studies argue that companies that are increasingly developing their knowledge management processes and intellectual capital tend to improve their entrepreneurial orientation over time and eventually promote innovation in firms that are supposed to explore in this research . Based oRegional economic growth and innovation knowledge are considered as basic inputs. In local and international markets, knowledge is an integral resource to improve competitiveness . AccordiEvery sort of knowledge that is acquired, developed, and disseminated must be accompanied by authorization and knowledge storage; otherwise, a company runs the risk of unintentionally forgetting to acquire knowledge . The tacKnowledge policies and strategies, according to the literature on knowledge management, have an impact on a company’s ability to innovate and persist . The antH1: There is a positive relationship between knowledge management and innovation.Intellectual capital is a collection of solid reserves including institutional explicit knowledge, skills, experience, technology, and contacts that enable an organization to compete in the sector . Roos 2. Human cThe connection and collaboration among individuals who share their ideas result in social capital . BuenechIn summary, human capital and social capital play an active role in the innovation activities of a firm. As intellectual capital has primarily focused on the utilization of resources in the organization , the RBVH2: There is a positive relationship between intellectual capital and innovation.A firm’s knowledge depends mainly on its internal and external environment and the environmental dynamism is a source of opportunity and resources . The comAs a result, a company that does not gain information from its peer relationships may miss out on the possibilities to better exploit entrepreneurial prospects, reducing the impact of these opportunities on performance . AccordiH3: There is a positive relationship between knowledge management and entrepreneurial orientation.Prior literature indicates that intellectual capital positively influences entrepreneurial orientation . EntreprSocial capital gives support to the development of entrepreneur activities; those organizations that have good social capital and entrepreneurial orientation behavior tend to have an influencing position in networks and thus these organizations have the tendency to grab more opportunities . Prior sThe most relevant characteristic of the RBV is the focus on the internal forces of a firm, emphasis on the proper use of organizational resources, and creating and managing intangible organizational resources in a proper manner . IntangiH4: There is a positive relationship between intellectual capital and entrepreneurial orientation.Entrepreneurial orientation refers to the strategic orientation of a firm and it represents how firms exploit knowledge-based resources to discover and exploit new opportunities . EntreprH5: There is a positive relationship between entrepreneurial orientation and innovation.In this study, we propose a mediation mechanism of entrepreneurial orientation in the relationship between knowledge management, intellectual capital, and innovation. First, we discuss the mediating role of entrepreneurial orientation between the relationship of knowledge management and innovation. Several studies tried to test entrepreneurial orientation as a mediator like self-concept characteristics and performance, leadership style and performance , culturaEntrepreneurial businesses are more inclined to pursue a variety of opportunities and venture into a variety of fields . AdditioH6 (a): Entrepreneurial orientation mediates the relationship between knowledge management and innovation.Second, the study discusses the mediating role of entrepreneurial orientation between the relationship of intellectual capital and innovation. Intellectual capital boosts the entrepreneur orientation activities because entrepreneur orientation decides on the base of organization resources, and intellectual capital is the intangible asset of the organization . MoreoveEntrepreneur orientation develops organizational knowledge and the ability to make a decision. H6 (b): Entrepreneurial orientation mediates the relationship between intellectual capital and innovation.Existing studies tested different moderators like knowledge intensity as a moderator between knowledge processes and innovativeness , innovatHowever, the above literature suggests that there are no or only a few studies that have investigated the impact of a leader’s education level on the proposed relationships. For example, H7 (a): A leader’s education will moderate the effect of knowledge management on entrepreneurial orientation such that higher levels of education will increase the strength of the positive association between knowledge management and entrepreneurial orientation.H7 (b): A leader’s education will moderate the effect of intellectual capital on entrepreneurial orientation such that higher levels of education will increase the strength of the positive association between intellectual capital and entrepreneurial orientation.The conceptual model depicting the relationships is given in This study used an exploratory research design. The nature of the study was cross-sectional and based on quantitative data. The study generated primary data through an email survey performed using a sample of Pakistani SMEs in the Information Technology (IT) sector. To select the sample, we chose IT firms registered in the company directory of the Pakistan Software House Association (PSHA). The PSHA has almost 896 active IT companies, which possess expertise in custom software development, enterprise resource planning (ERP), financial solutions, mobile content, document management, enterprise computing, and business process outsourcing. The major reason for selecting the IT firms is the high potential for innovation and greater contributions of these firms to economic development as well as in creating new opportunities for other business services. Moreover, intellectual capital and product innovation are more relevant for these companies than for service companies. The IT Sector of Pakistan is relatively well developed and has had a slightly better rate of growth in recent years. The data were collected through a structured email survey from the CEO, director of innovation, or from the top executives from July to August, 2020.Following the procedure of The knowledge management process is a combination of three elements including knowledge acquisition, knowledge sharing, and knowledge utilization practices . This stThe intellectual capital was measured using a 5-point Likert scale with the help of two dimensions, such as human and social capital. Human capital was evaluated through five items scale adapted from the study of This study operationalized entrepreneurial orientation through innovativeness and proactiveness. Entrepreneurial orientation was rated using a 5-point Likert scale. To measure innovativeness and proactiveness, we adopted five measurement constructs from the study by This study conceptualizes innovation as two-dimensional constructs, such as product innovation and process innovation. Innovation was evaluated using 5-point Likert scales. Moreover, to measure product and process innovation, we adopted 8-items scale from the study by To measure leaders’ education, we used 5-points Likert scale and 6 items were adapted from the study by The data were analyzed using the Smart-PLS software 3.0 version. This software is currently considered one of the suitable software to apply partial least squares structural equation modeling (SEM) . PLS-SEMTo test the reliability of constructs, Cronbach’s alpha, composite reliability, and AVE values were assessed. According to Additionally, discriminant validity was calculated using the Fornell–Larcker criterion as findings are expressed in R2, the structural model explained (13.7%) variance in entrepreneurial orientation and (25.5%) variance explained in innovation. As suggested by prior researchers the value of R2 and Q2 should be greater than 0.10 or zero . Therefore, H1 was accepted. Moreover, the results of H2 show that intellectual capital has a positive and significant influence on innovation with a standardized path coefficient . Thus, H2 was supported. Furthermore, the findings of H3 illustrate that knowledge management has a positive and significant impact on entrepreneurial orientation with a standardized path coefficient . Hence, H3 was accepted. Additionally, the results of H4 show that intellectual capital has a positive and significant effect on entrepreneurial orientation with a standardized path coefficient . Therefore, H4 was supported. Meanwhile, we tested H5 and the findings explore the entrepreneurial orientation that has a positive and significant impact on innovation with a standardized path coefficient . Hence, H5 was accepted.The study analyzed the hypotheses with bootstrapping to explore the significance level between all the variables. All the hypotheses were statistically significant and the results are shown in **, t = 2.926, p = 0.001; 0.075**, t = 3.565, p = 0.001). Likewise, to assess partial and full mediation effects, we followed the approach by Besides, we tested the indirect effect of entrepreneurial orientation in the relationship between knowledge management and intellectual capital on innovation. The results of H6a and H6b were presented to indicate that entrepreneurial orientation has an indirect positive and significant influence on the related knowledge management and intellectual capital on innovation with a standardized path coefficient . Thus, H7a was supported. Furthermore, H7a findings mention that leader education strengthens the association between intellectual capital and entrepreneurial orientation with a standardized path coefficient . Thus, H7b was not accepted.Lastly, we assessed the moderating effect of a leader’s education in the relationship between knowledge management and intellectual capital on entrepreneurial orientation. The findings are expressed in The knowledge-based view shows that as a rare, unique, valuable, and irreplaceable resource , knowledThe first objective of the current study is to find the statistical relationship between knowledge management, intellectual capital, and innovation in IT sector firms of Pakistan. Prior literature suggests that knowledge management and intellectual capital are significantly important for SMEs and IT sector firms, in particular. But knowledge management and intellectual capital are not well researched in the IT sectors of Pakistan. Therefore, based on prior literature and understandings, the researchers have developed and proposed the existence of a statistical relationship between knowledge management, intellectual capital, and innovation. This proposition is tested and verified through the empirical data and found significant, as is supported in previous studies  on knowlThis study aims to test the relationship between knowledge management, intellectual capital, and entrepreneurial orientation in the IT sector of Pakistan. The role of intellectual capital practices in terms of human and social capital are the important factors within the organization to trigger the entrepreneurial orientation. This research recognizes that appropriate knowledge management processes and intellectual capital practices among employees provide the firm with ultimate support in the industry. These study findings revealed a positive and significant relationship between knowledge management and entrepreneurial orientation similar to the study by Measuring the postulated relationship between entrepreneurial orientation and innovation is also the aim of the study. The study found a significant and positive relationship between entrepreneurial orientation and innovation. Previous literature suggests that an improvement in the entrepreneurial orientation of a firm can promote innovation. These findings are in line with the prior studies . For theThis research requires measuring the anticipated effects of entrepreneurial orientation as a mediator among knowledge management, intellectual capital, and innovation. The results suggest that entrepreneurial orientation proves as an area of substantial importance in promoting innovation in the IT sector of Pakistan. These findings argue that entrepreneurial orientation partially mediates the relationship between knowledge management, intellectual capital, and innovation, and the results are in line with the past literature .The current study considers the moderating role of a leader’s education in the relationship between knowledge management, intellectual capital, and entrepreneurial orientation in IT sector firms in Pakistan. The results show that a leader’s education significantly moderates the effect of knowledge management on entrepreneurial orientation. However, a leader’s education does not moderate the effect of intellectual capital on entrepreneurial orientation such that low levels of education will decrease the strength of the positive association between intellectual capital on entrepreneurial orientation. This finding is in line with the prior study by This manuscript contributes to the entrepreneurial orientation and literature on knowledge management, intellectual capital, and innovation. The research outlines the theoretical perspective that knowledge management and intellectual capital, support a firm to extend and deploy its entrepreneurial orientation to improve its innovation, especially if the firm operates in a turbulent market. In so doing, the study shows that the firm should use some consistent strategies for knowledge management and intellectual capital accumulation to build the desired innovation capacities. The theoretically derived research model, which links knowledge management, intellectual capital, and innovation, and also the moderating role of a leader’s education was empirically validated using an empirical study of 393 small and medium-sized firms in the IT sector of Pakistan.The study has advanced the theoretical contribution regarding knowledge management, intellectual capital, innovation, and entrepreneurship research in the following aspects. First, the research introduced knowledge management as an antecedent of entrepreneurial orientation and innovation to cover the existing gap. Moreover, this study measures intellectual capital as a first-order construct distinguishing it in human capital and social capital. Second, this study examined entrepreneurial orientation as a mediator between knowledge management, intellectual capital, and innovation, which was previously, not discussed by the prior researchers. Third, this research introduces a leader’s education as a moderator between knowledge management-entrepreneurial orientation and intellectual capital, entrepreneurial orientation relationships that were previously ignored in the relevant literature.Another notable contribution of this study is that it examined a mediator that was otherwise considered an independent variable traditionally and answered whether entrepreneurial orientation positively mediates the relationships between knowledge management process and intellectual capital on innovation. In this sense, the findings of the study demonstrated that entrepreneurial orientation characteristics, such as risk-taking, innovativeness, and proactiveness, are critical to the effective deployment of knowledge application and innovation. Furthermore, this study contributes to the existing literature as it examines the relationship in a context of a collectivistic country as these conceptions are earlier developed and tested in Latin America and Western culture . Hence, Moreover, this study provides implications for managers, entrepreneurs, and policymakers who are directly involved particularly in the IT sector of Pakistan. Knowledge management and intellectual capital seem to be more beneficial for firms with high levels of intellectual capital accumulation and knowledge-based resources. However, managers have to craft appropriate resource combinations to take advantage of both intellectual capital and knowledge to leverage the innovation and to translate it into superior firm performance. The creation of intellectual capital and knowledge help firms in harnessing product and process innovation as well as facilitates the firms to make it sustainable. To promote intellectual capital and knowledge management to get competitive advantages, managers can use different entrepreneurial mechanisms. Moreover, by focusing on intellectual capital, knowledge management, and entrepreneurial orientation, firms can set the grounds for generating new, innovative, and creative ideas as well as thoughts to be innovative. As the study recommends the mediating impact of entrepreneurial orientation, the owners and managers in SMEs, particularly IT firms are required to commit their complete potential to ensure entrepreneurial orientation and also motivate their employees to practice it.Furthermore, the study provides insights into the role of a leader’s education in promoting entrepreneurial orientation and innovation, particularly for SMEs who practice intellectual capital might improve innovation in their companies if they have a more qualified leader. As the innovation index shows that Pakistan is occupying a low position, the firms should hire more educated leaders to implement a successful innovation process in SMEs. The findings of the research are also important from a social perspective, particularly for the accumulation of social capital. Concerning intellectual capital development, the firms that form social capital are more likely to get success. Evidence for the argument can be found in the knowledge-intensive SMEs that are evident of heavy investment in resources, such as physical assets, strong relationships at personal as well as a team level, maintaining high trust levels, control norms, and strong networks across permeable limits. An investment in social capital enables a firm to foresee the market changes and meeting with customer demands more appropriately. Thus, understanding the social perspective helps managers to understand the social ties and invest in corporate social responsibility at the appropriate time.This study provides some limitations and future research directions for upcoming researchers. First, the study is cross-sectional; so the underlying analyses of results could be vulnerable. However, a longitudinal analysis is required to examine and elucidate the postulated relationship among study variables at different times. Second, the limitation addresses how the variables are measured. The study uses self-reported measures, raising probable issues of common method biases. However, self-reported questions are found to be more appropriate to measure the firm responses to the study variables. To address the common method biases issue, VIF is calculated using the procedure prescribed by The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by National Textile University, Faisalabad, Pakistan. The patients/participants provided their written informed consent to participate in this study.QY and SA proposed the research, analyzed the results, and wrote the manuscript. WJ, MM, and NS revised the whole manuscript and extensively edited the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This article contributes to the limited empirical literature on the impact of decentralization on psychological wellbeing by investigating the hypothesis which signifies that shifts toward more fiscal decentralization in health services would be accompanied by improvements in health outcomes. Formulating a conventional public finance model applied to health care, this hypothesis is tested on a panel data of the Pakistan's provinces during the period 1990 to 2015. The empirical underpinning of the article suggested that the economic reforms of 2001 in Pakistan's healthcare sector, through fiscal decentralization, have imposed a substantial and positive influence on the effectiveness of the public policy in improving the healthcare outcomes over the examined period. Decentralization has brought enormous changes in Fiscal structure of Pakistan since 2001 economic reforms. These reforms make a paradigm shift in social aspects of life. Though, decentralization is a wide concept but its analysis mainly analyzes the authority or structural shift. The politics and the process of devolution application require enhanced understanding to assess how federal government utilizes the decision space to bring positive change in healthcare system and future prospect challenges. Generally, people expect the future benefits and gains, the healthcare facilities, and their impact on psychological wellbeing are observed to improve . Further, this empirical approach is applied to demonstrate the above-mentioned questions .To empirically test the relationship between decentralization and psychological wellbeing of citizens by providing healthcare facilities to the local citizens , where FGLS is model used for the purpose of estimation  Total health expenditure per capita which is represented by FICi, t and shows the outcome indictor for the devolution efforts by the subnational government on the health expenditures.(b) Fully immunized childrenND/Pi, t and shows the outcome indictor for the devolution efforts by the subnational government on the health expenditures.(c) Number of doctors/paramedics as represented by NPPBi, t and shows the outcome indictor for the devolution efforts by the subnational government on the health expenditures.(d) Number of population on hospital bedDEi, t represents the benchmark devolution reforms time period, so we take 0 before 2001 and 1 after 2001 devolution reforms,Xi, t denotes the complete control variables that are taken for model estimation.i, t is used for time fixed effect and control of years.φt and i index year t and province i, respectively.We work with a balanced panel large T dimension relative to the number of provinces (the N dimension of the panel). For this reason, the use of a dynamic panel-like system and the difference general method of moments (GMM) bias the estimators. Thus, approximating with a dynamic model does not seem to be a good empirical strategy. Moreover, according to Greene , DavidsoThis study has taken psychological wellbeing and individual's perception as the outcome. Whereas psychological wellbeing is much broader concept. The research indicates that psychological wellbeing can be differentiated by its negative or positive effect on individual's life satisfaction –(4)  maintain positive (negative) and strongly significant coefficient vis-à-vis the devolution reform variable, which suggests that health services have increased in after the devolution reforms. In 2001–2002, slightly more than half of children in this age group had immunization coverage that in 2014–2015. Remarkably, the findings are coherent with theoretical prospects, and the results indicate that local governments are more directed toward the needs of general public in comparison with federal government Faguet, .The results of democratic dummy (1 denotes the democratic form of government and 0 otherwise) are positively associated with the health outcomes and health services. The contrary results are true for rural development. This positively emphasizes the democratic form of government. In this form, the local government elected by the local people in a democratic system is more in line with the needs of the people. Real GDP per capita is positive and significant for per capita healthcare expenditures and healthcare outcomes. Population is also an important feature that determines the scale effects in the provision of healthcare and shows positive relationship. Going forward, the allocation of government spending on public services follows a similar trend. The size of own-source income has a statistically significant positive effect on healthcare spending; however, for other some variables, the results were not statistically significant , 3.To make the results more convincing following best practices used by Kanbur and Zhang , EzcurraTo mitigate this potential bias, we use a fixed-effects two-stage least squares (2SLS) approximation to assess the impact of decentralization policies on per capita health service spending and outcomes variables. This approach allows us to leverage external tools to address endogeneity. However, it requires the presence of tools that are related to endogenous variables but not directly related to the error term. According to the literature, the tools used are social security and welfare, and investment in roads and highways, which capture the mechanisms by which fiscal arrangements can influence decentralization policies.p-values shown in Empirical results using the IV tool confirm the stimulating effect of decentralization policies on different components of health service spending and healthcare outcomes. Furthermore, the findings support the previous view that local governments provide appropriate resources to improve their spending in the health sector after removing endogenous factors. The Hansen J statistic for the over-identification test for instruments indicated that excluded instruments were correctly excluded, with The Execution of devolution policy in 2001 exemplifies a major step toward decentralization of social provisions in Pakistan. The purpose of this section is to discuss decentralization from a broader view and focus on the implications of psychological wellbeing through its per capita expenditures on health services and to construct indicators to gauge the favorable or unfavorable impact of devolution in Pakistan on various outcomes of health service provision.In Pakistan, healthcare facilities have been decentralized to local governments most commonly in past few decades, and its budgetary share is continuously growing. Therefore, improvement in this sector is perceived as a higher-order factor, the marks of these aspects produce psychological wellbeing, and it creates a broader view of psychological wellbeing , and other resources that facilitated us in completion of this research are greatly acknowledged.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.