Child and family social workers in the UK work closely with other agencies including schools and the police, and typically they are based in local authority offices. This study will evaluate the effectiveness of placing social workers in schools (SWIS) on the need for social care interventions. SWIS was piloted in three local authorities in 2018–2020, and findings from a feasibility study of the pilots suggests SWIS may operate through three key pathways: (1) by enhancing schools’ response to safeguarding issues, (2) through increased collaboration between social workers, school staff, and parents, and (3) by improving relationships between social workers and young people.The study is a two-arm pragmatic cluster randomised controlled trial building on three feasibility studies which found SWIS to be promising. Social workers will work within secondary schools across local authorities in England. 280 mainstream secondary schools will be randomly allocated with a 1:1 ratio to SWIS or a comparison arm, which will be schools that continue as normal, without a social worker. The primary outcome will be the rate of Child Protection (Section 47) enquiries. Secondary outcomes will comprise rate of referrals to children’s social care, rate of Child in Need (Section 17) assessments, days spent in care, and educational attendance and attainment. The study also includes an economic evaluation, and an implementation and process evaluation. Social care outcomes will be measured in July 2022, and educational outcomes will be measured in July 2023. Days in care will be measured at both time points.Findings will explore the effectiveness and cost-effectiveness of SWIS on the need for social care interventions. A final report will be published in January 2024.ISRCTN90922032).The study was registered retrospectively with the International Standard Randomised Controlled Trial Number registry on 13.11.2020 ( This study will evaluate a programme that aims to embed social workers into schools (SWIS) so that they can work more effectively with education colleagues and with children and families. Education and Children’s Social Care (CSC) have an important inter-agency relationship, and both play a vital role in keeping children safe and promoting their wellbeing. Schools have long been among the major sources of referrals to CSC in the UK, contributing the second highest proportion 18%) of all referrals in 2018/19, behind the police (29%) 8% of all. PolicymSWIS may be a promising approach to doing this. The central idea is that having a social worker linked to and based within a secondary school can have a range of benefits. In particular, to improve the service delivered to children and families, enhance interagency working, reduce risks to children and lead to better outcomes. Building on a recent set of three pilot studies , this prThe three pilot studies have been central to the development of the SWIS study. They took place in Southampton, Lambeth and Stockport in 2018–20. The pilot evaluation generated qualitative and limited quantitative evidence that the intervention was positively received by schools, social care, children and families . A rangeStudy research questions (RQs) relate to (1) impact evaluation, (2) implementation and process evaluation, and (3) economic evaluation.RQ1) Secondary research questions are: What is the impact of SWIS on:The primary research question is: What is the impact of SWIS in reducing rates of Section 47 enquiries , compared to usual practice? (RQ2)rates of referral to CSC and Section 17 assessments?  (RQ3)the number of days children spend in care?  (nd September 2020) (RQ4) and attainment (recorded June 2021 and 2022)? (RQ5)educational attendance , what evidence is there for the mechanisms of change identified in the logic model? (RQ7), and how does SWIS impact the wider social care system? (RQ8).Research questions in this part of the study are: Is SWIS implemented as intended? (RQ9).The research question in this part of the study is: What is the additional cost associated with SWIS and is it justified by improvements in student outcomes?  and mainstream secondary schools as the unit of randomisation.The study will be conducted in mainstream secondary schools in England, across 21 LA areas.Mainstream secondary schools are places of education for children aged between 11 and 18 (Years 7 to 13). They are funded by the Government and provide a free education for children, though a number of models exist, such as academies, free schools, and faith schools. All children attending the schools are eligible for the trial. Schools may opt-out of participation in the Implementation and Process Evaluation (IPE) while remaining in the trial.Informed consent will be obtained from all individuals who take part in aspects of the IPE (e.g. surveys and interviews). Researchers will take consent, and receive signed consent forms and/ or audio recorded verbal consent from participants.The intervention physically locates social workers within schools with the aim to build better working relationships with school staff, students and families. Rather than working with students and families from a LA office base, and liaising with and providing advice to education professionals remotely, the social worker is embedded in the school. Enhanced school response to safeguarding issuesIncreased collaboration between social worker and school staff, and parentsImproved relationships between social worker and young peoplePathway A is based around regular, good quality, communication between the social worker and school staff. The advice and support given to school staff is hypothesized to increase staff confidence in safeguarding issues and improve the quality of school referrals. Pathway B is about working directly with families and improving relationships between social workers and parents. Pathway C is about working with children and young people directly. Frequent interactions with the social worker enable the young person to trust the social worker and to feel understood and supported. This is theorised to lead to improved school attendance and participation, better management of a young person’s risks and improved outcomes. In all three pathways, improved child and family outcomes are theorized to lead to a reduction of the number of children in care.programmes@whatworks-csc.org.uk). This distils the key messages from the pilots into a practical format designed to assist the 21 LAs in delivering SWIS. Its stated aim is to provide “a framework to refine the SWIS Scale-up programme and encourage more consistent and effective integration of social workers into schools”, although the need for flexibility is acknowledged. The manual offers a number of recommendations for implementers. Among them, the following relate to programme delivery:Following the pilot evaluations, an intervention manual has been developed .The focus should be on statutory social work with additional opportunities for “preventative” aspects, which could involve “advising staff, families and young people” and working with children who are not at the threshold for formal involvement.Caseloads should be managed by the SWIS team manager and be in line with LA averages, and where possible the carry-over of existing caseloads prior to the launch should be minimal. To avoid disrupting existing relationships SWIS workers are expected to take on new cases.Social workers should be embedded in schools as far as possible, with their own office space in the school and opportunities to integrate.Face to face contact should be the basis for the intervention, in order to build strong relationships with school staff, children and families.The control group will receive service as usual. Children who are deemed by school staff to require the involvement of CSC will be referred to the LA, usually via telephone call or email to a Multi-Agency Safeguarding Hub arrangement or a referral and assessment team. Children judged by CSC to meet the threshold for involvement will be allocated a social worker as usual, but social workers will not be based in the school. This was chosen as the comparator because it is the status quo that the intervention can be distinguished from, and the system that SWIS needs to perform better than if it is to be considered worthwhile. Children who are deemed to require the involvement of CSC will continue to be eligible for this across all schools, including the control group. LAs will make case by case judgements about the best interests of children who were receiving a service prior to the trial. For example, a child on an existing child protection plan may retain their previously allocated social worker to avoid disruption, rather than transferring to the SWIS worker.We anticipate good adherence to the intervention, as the funding for the trial intervention  is provided to participating LAs and schools on the basis of compliance with the intervention guide supplied by the funder, and LAs have committed to delivering SWIS as described. All schools also confirmed their commitment at the outset. However, the adverse circumstances of Covid-19 may mean that some sites need to adapt SWIS, for example to accommodate social distancing or increased remote working. The IPE is designed to capture such adaptation and secondary analyses will explore the impact of varied levels of adherence.Child protection (Section 47) enquiries. Child protection (s.47) enquiries are investigations CSC carry out when they have “reasonable cause to suspect that a child who lives, or is found, in their area is suffering, or is likely to suffer, significant harm” . This is a key point in the work of CSC; an enquiry would normally involve an assessment of the child’s needs and the ability of family members or carers to meet them. Social workers would normally interview family members, children (if they are old enough), and use information from other agencies such as schools and health. This data will be collected from LA CSC departments, based on a data sharing agreement between each LA and the research team.Child protection (s.47) enquiry starts will be recorded as a binary (no enquiry/ enquiry started) variable. If the s.47 start date lies between and including the start date of the academic year , and 31st July 2022, it will be coded 1, otherwise it will be coded 0. Enquiries will be recorded at the individual-level, then aggregated at the school-level and shared with the research team. This aggregated count variable will be our outcome measure.The following CSC data will be collected from LA CSC departments, based on a data sharing agreement between each LA and the research team.Referrals to CSC. Referrals are made to CSC when someone thinks a child is at risk, and schools are typically the second highest agency referrer (after the police)  variable. If a referral date lies between and including the start date of the academic year , and 31st July 2022, it will be coded 1, otherwise it will be coded 0. This will be recorded at the individual-level, then aggregated at the school-level and shared with the research team. This aggregated count variable will be the outcome measure.Child in Need (s.17) assessments. A Child in Need assessment aims to identify the needs of a child or children within a family, and ascertain what support the family needs to meet them. Start dates for assessments will be recorded and assessments will be recorded as a binary (no assessment/ assessment) variable. If the start date for assessment lies between and including the start date of the academic year , and 31st July 2022, it will be coded 1, otherwise it will be coded 0. These will be recorded at the individual-level, then aggregated at the school-level and shared with the research team. This aggregated count variable will be the outcome measure.Days in care. We will record the total number of days children spend in care for two time periods, 23 months and 35 months, from (and inclusive of) the start date of the academic year . We will create two variables, one that counts the number of days spent in care between and including this date and 31st July 2022, and the other that counts the number of days spent in care between and including 2nd September 2020 and 31st July 2023. The inclusion of days in care at the 35 month timescale  is because impact on this outcome is more likely to become clear with a longer follow-up. This will be recorded at the individual level, then aggregated at the school-level and shared with the research team. This aggregated count variable will be the outcome measure.The following data will be collected from the Department for Education’s National Pupil Database (NPD), and will be made available to the research team in anonymised form at an individual level (identifiers will be stripped from the dataset) . One appEducational attendance: Unauthorised absences (%). This is the percentage of sessions  that children are absent without being authorised, out of the number of sessions possible. The data are available via the NPD absence dataset. For the 2020/21 academic year, these data will be released by the NPD in March 2022, and for the 2021/22 year they will be released from October 2022 (unamended) to March 2023 ; access will be requested for the final datasets for both years in April 2023. This variable will be defined by the number of sessions missed due to unauthorised absence per term  out of the number of sessions possible per term.Educational attainment at Key stage 4. Key stage 4  results are the key educational outcome measure for secondary school pupils. Educational attainment at Key Stage 4 (KS4) for the 2020/21 academic year will be released in April 2022, and in April 2023 for the 2021/22 academic year; access will also be requested for both years in April 2023. The following outcomes will be reported for all pupils completing GCSE exams in 2021 and 2022 or subject to equivalent grading exercise :○ Attainment 8 –a measure of a pupil’s average grade across eight agreed GCSE subjects○ English Baccalaureate Average Point Score–an agreed set of GCSE subjects ○ % English and maths, grade 5 and above. This will be a binary variable coded 1 if the pupil achieved a level 5 or higher in both English and maths, and coded 0 otherwiseAt the study design stage, the funder advised that a minimum of 280 mainstream schools would be available to be randomised. Assuming an average of 925 students per school, an average base rate of 12.6 s.47 enquiries per 1000 students per school year under usual practice conditions, and a between-school coefficient of variation of 0.45 within LAs  [Participating LAs were chosen through a competitive tender process managed by the funder. Each chosen LA invited schools and gained the agreement from up to 16 schools to be put forward for randomisation. Recruitment of schools was completed for each LA before the list of schools was passed on to the trial statistician for randomisation. The statistician was not directly involved in the recruitment of schools. Schools were considered recruited once the LA confirmed that they have agreed to take part.At the beginning of the study the research team received a list of up to 16 schools which had agreed to take part from each LA. The trial statistician performed the randomisation as described below, and the research team communicated the allocations to LAs via the funder.Mainstream schools were allocated to SWIS or usual practice in a 1:1 ratio whilst minimising covariate imbalance within and across blocks using a balancing algorithm for cluster randomised trials with multiple blocks , with LASchool size . The size of the school and number of students is likely to have an effect on how the social worker engages with and works within the school. In a larger school their time and resources may be spread across more students, and there may be more professionals to work with. It is reasonable to expect that this is a factor that will shape the implementation of SWIS and therefore balancing school size between groups is sensible.Proportion of students eligible for free school meals. FSM are provided for children resident in lower income households. They are designed to ensure that all children have access to adequate nutrition when they are at school. Eligibility for FSM is a reliable indicator that a child is from a low income household [ousehold . This isousehold .A proforma will be supplied to the LA, who will complete and return it to the research team at each data collection time point. This process will be trialled in January 2021 prior to quarterly data transfers thereafter. Data relating to the economic evaluation  will also be collected in this way. Other data items required for the economic evaluation will be collected directly from individual social workers through online surveys (e.g. activity time spent implementing the intervention).Educational outcome data will be collected from the NPD and made available anonymised at an individual-level (identifiers will be stripped from the dataset) . One appDate            MilestoneJuly 2022                        Intervention endsJanuary 2023            Interim report: impact on CSC outcomes, IPE and economic analysisApril 2023            Key Stage 4 and Absence data releasedJuly 2023            Days in Care (35m) data collection endsJanuary 2024            Final report: impact on educational and days in care outcomesData for the IPE will be collected primarily through online surveys and interviews. Online surveys of social workers and school staff  to gather programme-level data on implementation and attitudes and to assess how SWIS is implemented in each school (RQ6 and 8). These will be short, taking no more than fifteen minutes to complete, and administered via Qualtrics online survey platform. Interviews will form part of purposive LA case studies .The following management plans are in place for each type of data.Data sharing agreements will be set up between LAs (Data Controller) and Cardiff University to enable sharing of data related to CSC. Data fields will be included in this data sharing agreement, data will contain no identifiers except for a school ID and/or trial allocation. A data lead will be identified at each LA, and the trial data manager will liaise with each data lead to confirm required fields. Building on our learning from the pilot, each LA will pilot data transfer during the set-up phase to ensure the data can flow and is in the correct format. These checks will be detailed in the data management plan. Once any issues are addressed in this pilot, data will be shared quarterly up to the end of the intervention (July 2022). A final data extract will be requested Summer 2023 for the days in care outcomes. Data will be reported by school year group and by month and provided to the Cardiff team in a proforma developed and agreed during the pilot. Data will be aggregated, and no individual-level data will be sent. These data will be securely transferred to Cardiff University and checked by the data manager. All data will be stored on Cardiff University servers in restricted folders available only to those on the trial team who require access. This will be detailed in the delegation log.As per the data sharing agreement with each LA, data on costs will be sent to Cardiff using the same proforma developed for the CSC outcomes. This will be checked and prepared for onward sharing to the economics team at the University of Oxford.Education attainment and attendance data will be accessed via the NPD. LAs will confirm the school identifier to the Cardiff team  to enable NPD to identify pupils in those schools. Education data for those pupils will be made available to the study team via their Secure Research Service–a remote access data safe haven  as per their data sharing processes .Trial data management staff will liaise with LA data leads and monitor proforma submissions at the specified time points. Progress will be recorded in a tracking system and all submissions will be quality checked. Automatic electronic validation checks built into the proforma will ensure data entered by LA staff complies with the specified formatting. Strict data checks will also be completed upon receipt of data collection proformas. The data manager will conduct data cleaning at each time point to ensure there are no missing/duplicated data or any outliers. Data cleaning will be a regular process and data queries will be raised with LAs if any discrepancies are found. All data queries will be logged within the tracker, and an audit trail maintained recording any changes to the data. Upon completion of data checks, the data manager will add the data to the master dataset and log the process as complete in the tracker.Personal data will be transferred to Cardiff University for the IPE. Personal data (name and contact details) of social workers, school staff and children will be transferred to Cardiff University by LAs for those who have agreed for this information to be sent. Study information will be made available to IPE participants prior to participation. A risk assessment has been signed off to mitigate any potential risks regarding confidentiality and identification while processing these data, and personal data will be stored on secure university network servers, with access limited to members of the research team. No personal data will be processed by Cardiff University for the impact evaluation or economic workstream. All data will be stored in a secure manner and processed in accordance with data protection legislation (in accordance to GDPR and UK DPA 18) and Good Clinical Practice (GCP).The elements and timing of data collection are shown in All primary analyses will be ‘intention to treat’ i.e. schools will be analysed in the groups to which they were randomised, regardless of adherence to the intervention. Missing outcome data will not be replaced. For the primary outcome analysis, a quasi-Poisson regression model will be fitted with fixed effects for LAs and cluster robust standard errors reflecting the clustering structure (schools within LAs) to compare rates of s.47 enquiries at 23 months by arm, using the number of students per school as the exposure scaling variable, and s.47 enquiries for 2018/19 (baseline) and % eligible for FSM as fixed-effect covariates. Using a quasi-likelihood model accounts for possible over- or underdispersion i.e. variance in the data that is higher or lower than expected under the standard Poisson model. Allocation will be included in the model as a fixed effect, and the intervention effect (model coefficients transformed into rate ratios) will be presented as point estimate with two-sided 95% confidence interval (CI) and p-value. If the estimated rate is lower in the SWIS arm than in the comparator arm and the 95% CI around the estimated rate ratio excludes the null effect (rate ratio = 1), effectiveness of SWIS at the 5% level will be concluded.Secondary outcomes will be analysed in a similar way as the primary outcome, by fitting regression models with cluster robust standard errors depending on the type of outcome: quasi-Poisson for rates  and linear for continuous variables . For the school-level variables  we will include the same fixed-effect covariates in the model as for the primary outcome  and also use the number of students per school as the exposure scaling variable. For the student-level variables  we will additionally include gender and ethnicity as covariates, and the baseline outcomes used will be 2018/19 attendance (for attendance) and KS4 results (for attainment). The p-values generated from the secondary outcome analyses will be adjusted for multiplicity using Hochberg’s step-up procedure.A secondary analysis will use per-term  outcome data and include term (or month) as an additional covariate in the model to explore potential implementation effects and/or seasonality. Another secondary analysis will assess the hypothesised mediators of change outlined in logic model  at the 2Missing outcome data will not be replaced in the primary analysis, but we will perform null imputation for missing covariates, where we will replace missing values with 0, and create a dummy indicator for the covariate coded 1 if the value was missing, and 0 otherwise, and include the dummy in the regression. If more than 5% of outcome data are missing, we will consider repeating the primary analysis after multiple imputation. To assess the impact of non-compliance (if present), we will exclude intervention arm schools that did not adopt the intervention at all and then repeat the primary analysis, and we will also perform a complier average causal effect (CACE) analysis. Statistical code and anonymised data can be provided upon reasonable request, in line with Centre for Trials Research policy.The study has ethical approval from School of Social Sciences Research Ethics Committee of Cardiff University . Written, informed consent to participate will be obtained from all participants. Due to the use of remote methods, and in case consent forms are not returned, verbal consent will be audio recorded in interviews with professionals in addition to written consent.If interviewees say anything that makes the researcher concerned about harm to the participant or another person, then they have a duty to take appropriate action. In the first instance, usually this would involve discussing the concern with the Chief Investigator or a co-investigator. Depending on the nature of the harm, referrals to agencies may be appropriate, for example a referral to the LA CSC may be deemed necessary if a child was thought to be at risk.LAs are classed as ‘recruited’ once they have signed the data sharing agreement. Recruitment began (data sharing agreements sent for signature) on 22 October 2020. Recruitment was completed in February 2021. The trial was extended twice by the funder, first in August 2021 and then in January 2022 (see below) Amended data sharing agreements to cover the extended period were circulated to LAs for signature n September 2021 and March 2022.The SWIS trial is the largest and most rigorous study of school based social work in the UK, and will be a significant addition to the international evidence base. It will contribute to our understanding of how multi-agency responses may help protect children and reduce the need for CSC involvement, and investigate whether working in this way has an impact on educational outcomes for vulnerable children. However, this is a time of great disruption as a result of the Covid-19 pandemic, and our findings will need to be considered in light of this.Covid-19 is likely to present ongoing challenges for both the implementation of SWIS and our efforts to study it. As the implementation guide states, the intention is that “face to face contact should be the basis for the intervention” but this may simply not be possible. Although most schools remained open for ‘vulnerable’ children at the start of 2021 , many chMoreover, some of the outcome indicators we intend to use may not be comparable to the same measures in previous years. Attendance and attainment measures, for example, appear especially incomparable with pre-pandemic times. For example, whether or not an absence is unauthorised will be more difficult to record, and Key Stage 4 examinations will be replaced with teacher assessed grades in 2021 .At a broader level, the amount of disruption implementers face will make the results of our between groups analysis difficult to interpret. Covid-19 has increased the risk of misleading results, as (1) the implementation of SWIS is likely to be more variable and (2) exogenous factors may override the impact of the intervention. The IPE is designed to insure against these risks, and in this context the secondary and exploratory analyses that it will feed into will gain importance. It would be easier to address these challenges if the trial ran for more than a single academic year. However, as it is linked to a policy initiative the study is limited to the timescale set out by the funder.The study was extended twice by the funder, first in August 2021, and then in January 2022.This protocol was amended to accommodate the new timeline. This document reflects the amended study design. Both the original protocol (version 1) and the revised protocol (versions 2 and 3) can be found on the OSF. Key amendments made in the updated protocols are:Timing of analysis and reporting updated to reflect the extended intervention period .Changes to the data collection activities in the IPE, to reflect the fact that all case studies were forced online by the pandemic and conducted remotely. This meant that as of September 2021 no observations or child interviews have taken place, and these are now scheduled for terms 4 and 5.Additional data collection activities in the three extra terms, e.g. interviews with key decision makers in LA ‘front door’ teams.The use of the 2018/19 school year as a baseline, rather than the 2019/20 school year as originally planned. This is because the 2018/19 year is the most recent year not affected by the Covid-19 pandemic and therefore a more realistic baseline to use.Changes to the economic evaluation to reflect discounting to costs beyond 12 months from the start of the intervention, to present values using nationally recommended discount rates.https://whatworks-csc.org.uk/. We will also disseminate the study widely by other means, using in-person methods , electronic methods  and by publishing in academic journals.We are contracted to report the findings in reports that will be published open access by the funder at S1 Appendix(DOCX)Click here for additional data file.S2 Appendix(DOCX)Click here for additional data file. 1 Mar 2022The SWIS Trial: protocol of a pragmatic cluster randomised controlled trial of school based social workPONE-D-21-32030Dear Dr. Westlake,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. 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For more information, please contact Kind regards,Lucinda Shen, MScStaff EditorPLOS ONEJournal Requirements:1. Thank you for stating the following financial disclosure: https://whatworks-csc.org.uk/ Certain aspects of the study design were included at the request of the funder  received funding from What Works for Children's Social Care to undertake the research. Please state what role the funders took in the study.  If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript." If this statement is not correct you must amend it as needed. Please respond by return e-mail so that we can amend your financial disclosure and competing interests on your behalf.Reviewers' comments: 18 May 2022PONE-D-21-32030 The SWIS Trial: protocol of a pragmatic cluster randomised controlled trial of school based social workDear Dr. Westlake:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. 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Medical emergency response staff are exposed to incidents which may involve high‐acuity patients or some intractable or traumatic situations. Previous studies on emergency response staff stress‐related absence have focused on perceived factors and their impacts on absence leave. To date, analytical models on absenteeism risk prediction use past absenteeism to predict risk of future absenteeism. We show that these approaches ignore environment data, such as stress factors. The increased use of digital systems in emergency services allows us to gather data that were not available in the past and to apply a data‐driven approach to quantify the effect of environment variables on the risk of stress‐related absenteeism.First, machine learning techniques are applied to identify the importance of different stressors on staff stress‐related risk of absenteeism. Second, the Cox proportional‐hazards model is applied to estimate the relative risk of each stressor. Four significant stressors are identified, these are the average night shift, past stress leave, the squared term of death confirmed by the Emergency Services and completion of the safeguarding form. We discuss counterintuitive results and implications to policy.We propose a two‐stage data‐driven framework to identify the variables of importance and to quantify their impact on medical staff stress‐related risk of absenteeism.  Paramedics or emergency staff are frequently exposed to incidents that are detrimental to mental health  model to identify key stressors in the risk of absenteeism and the use of Cox regression Cox,  for abseThe paper is organized as follows. In section 1.1R2 was 31.8%  could be used to predict sickness absence in a statistically significant way. A one unit increase in the standard deviation of decision authority could lead to a reduction of number of sickness absence incidents by 13% ; for example, whether taking a new medicine can extend the survival time of a cardiovascular patient. Unlike other models for survival analysis, not only categorical variables but also continuous variables can be included in the Cox regression model. Since Cox regression is a branch of survival analysis, it can consider the event status (whether the event occurs or not) and the time before the event occurs. One advantage of Cox regression is that there is no strict requirement for the data distribution. Cox regression can use data that are not normal‐distributed  represents the hazard of concerned event at time t. This is calculated using the following equation:h0(t) is the baseline hazard function. It indicates an individual's hazard at time t when all the covariates included in the model are equal to 0. x1 to xn stand for the covariates of the event of interest. b1to bn measure the effect of the covariates. The baseline hazard function and coefficients of covariates are all estimated by using the method of maximum likelihood  curve, and areas under the ROC curve (AUROC) can reflect the model's discrimination in some way. The ROC curve and the AUROC were regarded as the measures for discrimination in this study. The ROC curve plotted sensitivity versus 1 – specificity for each possible cut‐off where sensitivity indicated the true positive rate on the y axis and 1 – specificity stood for the false positive rate on the C‐index) is used to solve this problem. The C‐index is widely used to reflect the predictive ability of survival analysis  in the United Kingdom. The focus was on stress that was reported among the ambulance staff and the stressors that the ambulance staff confronted in their daily work. The study population was all the ambulance staff working in the SCAS, which consisted of 1431 individuals.The variables included in the model were identified based on discussions with one Business Intelligence Analyst at SCAS, one 999 Business Intelligence Analyst, and interviews with the Head of Operations Berkshire West and lead for SCAS trauma risk incident management (TRiM) team. The TRiM team provides support to SCAS staff who have been involved in traumatic incidents. Staff can self‐refer, or their manager can refer them for support. The interviews gave us very valuable insights into what staff find traumatic; this directly influenced the identification of incidents that involved child death, childbirth, and safeguard (such as abuse). Details of the data collection are presented in the following sections.3.1The data related to staff members’ private information were all anonymous. A pseudo staff ID was used by SCAS data controllers to refer to individual staff members. Ethical approval for this research was obtained from the University of Southampton Ethics Committee. The Ethics and Research Governance Online (ERGO) number was 52537.The original secondary data provided by SCAS included four tables from different data warehouses: (i) stress‐related leave records; (ii) staff information; (iii) incident records, and (iv) work shift records. The data period was from January 1, 2016 to December 31, 2018. In the incident records table, the information recorded stressful incidents attended by each member of staff on each day during the observation period. The work shift table included the work shift that each staff member attended on each day during the observation period. The content included in those tables are presented in Tables The safeguarding form is a document in either electronic or paper format that ambulance staff must complete if they feel there is a risk to the patient or someone at the scene, who requires a referral to the police, fire, or social services. This type of incident may impose pressure on staff because patients are continuously exposed to some risk that may make them suffer the same incident again. For example, if the patient is abused financially, emotionally, or physically, ambulance staff do not have the authority to stop the incident as they can only provide medical care. In such situations the medical staff can inform the police via a safeguarding form; however, this process can be frustrating and stressful.3.2To conduct analysis, the original data were aggregated into a single table by linking their pseudo staff ID. To apply the Cox regression model, all the data must have the same format as the survival data. Namely, there was a column called “duration” to record the actual time when staff were exposed to the stressors before the concerned event happened. The stress‐related leave status column denotes, up to the duration time, whether or not the staff member took stress‐related leave at that point in time.To avoid the information loss from Cox regression, if a staff member has more than one stress‐related leave, the other days of stress‐related leave are considered as new entries into the dataset. It is assumed that a staff member will return refreshed after a stress‐related leave. For example, a staff member that has taken two periods of stress‐related leave within the observation period will have three records in the final dataset. Each record corresponds to the number of incidents a staff member attended between two absences or between the start/end dates of the observation and absences. Figure However, since the staff member did not take any stress‐related leave at the moment of the end of the observation, the stress‐related leave status of the third record was zero. Similarly, if a staff member does not take any stress‐related leave during the observation period, they will only have one record in the dataset over the duration of the whole observation period.The dataset used by the RF had the same format as that used by the Cox regression, except that there was no “duration” column in the RF since these two models are not able to capture the time of event.First, find all the stress‐related leave records, work shift records and incident records of each staff member. Second, assign incident records and work shift records to the corresponding working period of each staff member. Third, generate the table by accumulating the number of incidents a staff member attended within each working period. In order to manage all the incidents and work shift data within each working period of each individual staff member, the data were organized by using the index position in each dimension of the data array as shown in Figure Often, secondary data are not recorded in a format suitable for modelling and analysis. The process involves three main steps. After data aggregation, the new dataset consisted of 14 independent variables, one extra piece of information for Cox regression, and one dependent variable. Besides the demographic factors, the other factors represented the number of related stressful incidents that an ambulance crew attended within a certain period. For example, if the value of “Child Death” is 5, it means that the staff member has attended five incidents involving child death within a certain period. The data generated are presented in Table The average night shift was calculated by dividing the total number of night shifts by the total number of days that the medic worked.To conduct the analysis more efficiently, variables which contain strings were encoded into numerical values. The machine learning algorithms and Cox regression treat these variables as categorical variables. This is required by most of the model algorithms. A summary of the encoded variables and their corresponding numerical values is presented in Table 3.3Before further analysis, it is important to check the completeness of the dataset. The missing values analysis identified four variables which had missing values: gender, position title, role, and tenure. Their missing percentages were 10.5%, 10.5%, 10.5%, and 14.3%, respectively, see Table p value lower than 0.05. This meant that the missing pattern of tenure was not missing completely at random. Instead, tenure might have some correlation with other variables.After getting a general idea of the missing values in the dataset, it was crucial to learn more about the missing values pattern. In this case a Little's missing completely at random (MCAR) test can shed some light on this concern. Since gender, position title, and role were categorical variables, tenure—which was the only quantitative variable—can be tested. The result of Little's MCAR test for tenure gives an expectation maximization mean of 104.2 and a In terms of filling in the missing values, different methods should be wisely chosen according to different situations. For gender, the percentage of females and males was almost the same, but the number of male staff (45%) still slightly exceeded that of female staff (44%). Meanwhile, those staff with unknown gender accounted for 11% of the population. Those with unknown gender were encoded into a new category filling with 0.5.For role and position title, to avoid the issue of bias on existing groups, missing values were all replaced by “Others.”3.4During the data processing, we noticed some outliers in the dataset. For example, some of the staff members’ tenures were negative, which was not correct. The reason for this kind of outlier was that these ambulance staff joined the SCAS after the end of the observation period. Another reason was that some staff had taken stress‐related leave before their first start date at work. Moreover, some of the records also showed that some staff members took more than one stress‐related leave in the same time period. These occurrences generated some problems when extracting the data from the data warehouses. All these abnormal cases were deleted to form a final dataset for further analysis with less bias.4In this section we present descriptive statistics of the participants in this study. We then present results of the classification and Cox regression methods.4.1The number of ambulance crew from SCAS involved in this study was 1431. By aggregating the information from various datasets, the number of records in the final dataset was 1892. Among the staff, more than 76% did not take any stress‐related leave from 2016 to 2018; 17% took one day of stress‐related leave; 5% took 2 days of stress‐related leave; and 2% took more than three days of stress‐related leave.paramedic but there were still various other roles served in the team . This uneven distribution might indirectly denote that the incident rate of this kind of situation was low.The number of individuals who have not experienced an incident related to abnormal maternity delivery was larger than those who had encountered this situation  = 378.03. This means that the predictive ability of Cox regression was good: better than random prediction.Instead of using the ROC Curve, 2 = 337.30 and df = 8, p < 0.05. In other words, the Cox regression model may underestimate or overestimate the probability of taking stress‐related leave. The calibration of Cox regression was deficient.The calibration plot of Cox regression is shown in Figure 5The findings of this study do not concur with some of the studies in the related industry. Two research papers about occupational stress in emergency departments demonstrated that death or sexual abuse of a child, inability to provide optimum care, violence against staff, heavy workload and poor skill‐mix, mass casualty incident, or environment concerns ranked as the top six stressors  to certain types of jobs. All staff are well trained and are able to deal with the types of incidents that are typically described as stressful. If staff members had self‐reported as being very stressed (or were referred to TRiM–SCAS Trauma Risk Incident Management) they could be individually managed and perhaps allocated alternative (or “light”) duties until their stress levels had lowered sufficiently for them to return to full operational duties. In this research we assume that, after taking stress‐related leave, the staff member is refreshed. This may not be the case for all medical emergency response organizations as this depends on the risk management processes adopted by the organization and, in particular, the perceptions of what are “acceptable” stress levels for a staff member to return to the front line.The works of Elder et al.  and RossIt is possible that we have missed some important variables in our model either because they are not recorded or because we do not have access to the relevant digital system. Factors related to stress such as violence against staff (Ross‐Adjie et al., We have collected data from four digital platforms but with the increasing number of digital platforms available, it is possible that we have not included important variables in our model that are recorded in some form of digital system. Two examples of such data is the speed of the ambulance and the traffic intensity. Future research should explore the benefits of including other environment data.Past stress‐related leave records can show the pattern and trends of stress‐related leave, and can be a reference to future stress‐related leave prediction. Although a few studies focused on predicting stress‐related leave (Nieuwenhuijsen et al., Middle—but the relationship between this kind of incident and stress‐related leave is not evident. This may be due to the problem of class imbalance. Fernandez et al. (Several studies also mentioned that death or sexual abuse involving a child was extremely stressful but occurred at a very low frequency (Burns & Harm, z et al.  and Leevz et al.  present z et al. . Future The results of Cox regression showed that, of the six most important factors identified by RF, only four factors—safeguarding form, average night shift, past stress‐related leave, and death confirmed by EMS—had an actual impact on stress‐related leave. Length of tenure and incident related to mental health diagnosis had no impact on stress‐related leave. The ensemble algorithm of RF might be more accurate and convincing. The relationship between stress‐related leave and average night shift, and past stress‐related leave and death confirmed by EMS were not singly linear. The relationship involved square terms and cubic terms of the variables in the hazard function. It was rational that the hazard function was complicated because the probability of stress‐related leave was hard to predict and the mechanism underlying how a factor triggered stress‐related leave was difficult to depict by linear term only.Another limitation of this study is the lack of internal validation. Due to the nature of the RF that has an inherent bootstrap technique to avoid overfitting, it does not need internal validation. The bootstrapping technique could generate several samples with different data structures by random sampling with replacement. However, Cox regression might need internal validation for modifying the over‐optimism. It is commonly believed that the prediction model will predict more accurately in the subjects used to create the model than in new subjects (Roelen et al., 6This study examined the factors that eliciting stress‐related leave and established a model that helps the stress‐related leave prediction. By doing so, SCAS can gain more insight into enduring the provision of appropriate mental support to their ambulance staff, and ensure they are better prepared to deal with the labor loss when some staff really need stress‐related leave. SVC, RF, and Cox regression were applied to address the concerns. There were 13 independent variables in the RF model. These independent variables were selected based on literature and advice from SCAS team members. Only the six most important variables generated from RF were considered in the Cox regression model. Three models were discussed separately based on the following.In this study, RF identified the six most important factors that significantly contribute to the stress‐related leave prediction. They were average night shift, tenure, the number of incidents related to safeguarding form, the number of incidents related to mental health diagnosis, past stress‐related leave record, and the number of incidents related to death.Rather than considering only the first stress‐related leave of each staff member, all the stress‐related leave records of each staff member are utilized to establish the Cox regression model, which fits better with the real‐life situation. In terms of measurement of the model performance, both discrimination and calibration are assessed.The population of this study is homogeneous and limited to the ambulance staff from SCAS. While the results do not necessarily apply to medical emergency response staff from other organizations, the methodology can be applied to any emergency response services—for example, fire fighters, sea rescue, policing, and even the military.In the immediate future the findings of this research can be used to inform operation risk management. That said, the longer‐term implications can inform law and contractual arrangements, and emergency services’ policies.We would also like to show our gratitude to colleagues at the South Central Ambulance Service in the United Kingdom for sharing their wisdom with us during the course of this research, we thank the two anonymous reviewers for their insights and the editor for managing this submission.


Furthermore, caregivers evaluated the care they could administer with the device as being better than that associated with manual care.Low back pain among healthcare professionals is associated with the manual handling of patients. Some bed features for turning and repositioning have been developed; however, the load during patient care remains heavy. We developed a device to reduce low back load in caregivers during patient bedside care and evaluated it objectively and subjectively from a caregiver’s perspective using a randomised crossover study. Overall, 28 clinical nurses and care workers were randomly assigned to two interventional groups: administering care with (Device method) and without  the device in an experimental room. We measured the caregiver’s trunk flexion angle using inertial measurement units and video recording during care and then defined a trunk flexion angle of > 45° as the threshold; the variables were analysed using linear mixed models. Subsequently, participants responded to a survey regarding the usability of the device. Trunk flexion time and percentage of time were 26.5 s  ( As the population ages, consequent shortages in nursing personnel become a serious problem4. Certain types of care for bedridden patients, such as repositioning the patient’s body or turning them to change diapers or sheets, impose a heavy burden on caregivers5. This can cause severe low back pain. Patient handling is a risk factor for the development of low back pain6.Ageing is a major issue in many countries8. The prevalence of low back pain, including work-disabling low back pain, is higher in nurses than in people from other occupations11. Hence, patient-handling guidelines indicate the use of assistive devices or the cooperative efforts of two or more caregivers13, but caregiver shortages make these guidelines difficult to follow. Nurses working in hospitals and nursing homes, especially in Japan with an ageing population, often performed nursing care such as transfers and repositioning by one person14.Thus, more than 50% of clinical nurses and healthcare workers experience low back pain15 to reduce caregivers’ physical stress associated with turning and repositioning patients, such as a friction-reducing turning sheet with a wedge foam16, a mattress with turn-assist features via inflated air bladders17, and a mattress-sized turning platform18. As the main purpose of these devices is to avoid pressure ulcers, they only laterally turn the care recipient. This results in a shallow tilt angle or insufficient space between the air mattress and patient’s back, which is not useful for routine activities, such as changing linens and diapers or bathing. Therefore, we developed a prototype of a breakthrough, assistive device that has been programmed to laterally turn (on the long axis of the body) and support a bedridden patient’s body during direct care.Recently, some devices for repositioning and turning have been developed and made available commercially19 we first conducted in-field observations and identified the reduction of caregiver burden in changing positions and diapers as an unmet need in the field. We then began developing assistive devices that do not require more effort than conventional care, while still using assistive devices. The ‘Programmable Lateral Position Changer’ does not require special preparation, such as placing a particular sheet under the patient’s back or setting up a mechanical lift. Furthermore, it is designed to reduce caregivers’ burden so that one caregiver can provide care, offering an alternative method of patient handling to overcome problems experienced by many caregivers using the conventional manual method, which comprises manual labour without assistive devices.Using the bio-design approach of focusing on needs and using the most efficient methods to achieve them,The Programmable Lateral Position Changer has two main features. Raising part of the mattress allows the device to tilt the patient’s body laterally Fig. a and supThe device consists of a mattress and an underlying tilting unit attached to the bed frame with fixtures Fig. a. The ‘mThe tilting unit includes three movable plates and two inflatable/contractible airbags. Three movable plates, composed of a honeycomb structure sandwiched within an aluminium frame, have a hexagonal shape, are connected by hinges and two airbags are placed between hinged plates that can be folded into a Z-shape Fig. a,b,c. ThThe tilt angle is controlled in the range of 0° to 60° by converting data from the angle sensor attached to the upper- centre part of the movable plate. The tilting angle was calculated from the direction of gravity based on the detected values of the MPU-6050 tri-axis acceleration sensor  attached to the upper movable plate. If the sum of the measured acceleration values of the three axes is not 1 G, the sensor is judged to be faulty, and the power turned off. The error is assumed to be within ± 2%. The tilting angle can be programmed based on the type of nursing care or the patient’s characteristics (such as body weight or body conditions). Caregivers can easily operate the system by pressing once one of four buttons on the remote control. For example, the angle can be set to 45° during nursing care (diaper changing) and 20° during rest time  left, large (45°) right, small (20°) left, and small (20°) right; Fig. The tilting speed is controlled by the airflow rate inside the airbag controls, which can also be programmed beforehand. The maximum slow speed is 17 s for a 45° tilt.This study aimed to objectively and subjectively evaluate the device from a caregiver’s perspective.This was a comparative study with a randomised crossover design. We compared the outcomes between two care methods: care using the prototype by one caregiver (hereafter ‘Device method') and conventional care without the device by one caregiver . The nursing care procedure was as follows: participants (caregivers) laterally turned a care receiver from the right lateral decubitus position to the left lateral decubitus position to simulate the process of changing a patient’s diaper Fig. .Figure 7There were two sequences for performing the two care methods: one sequence is the Device method, which is first and the Manual method, which is second, and the other sequence is reverse. All participants performed both the Device and Manual methods once in either sequence. Participants were randomly assigned at a 1:1 ratio to a sequence according to their arrival order at the experiment room. For randomisation, we used a randomised allocation table that generated random numbers using a software program operated by an independent researcher. The table for random allocation was sealed and kept in a locked drawer until the day of the experiments. An independent researcher enrolled participants, assigned them to interventions, and explained the procedure for using the device. Therefore, blinding was not possible in this study. There was no important change in the methods after the study commencement, including eligibility criteria.22, the heart rate of caregivers indicates their physical burden, and the heart rates were 128 and 95 bpm while turning and changing pads, respectively23. In addition, the heart rate recovery time from peak heart rate to baseline heart rate, was a minimum of 3 min25. Based on these studies, we set a 3-min interval as a washout time.Additionally, we used a 3-min interval between the two interventions as the washout time to avoid carrying over the caregiver’s physical burden to the second intervention. According to previous studiesWe included care workers and nurses aged 20–60 years, with clinical experience in lateral turning and changing diapers who worked at the facility where the study was conducted in Osaka, Japan. We excluded care workers and nurses with health conditions that might have interfered with the care protocol and those with severe low back pain, which might have worsened by participating in the study.The interventions were performed in a simulated patient room at the facility between November and December 2020. The standard bed frame was equipped with an electric switch that raised the bed height, and the left and right bed fences could be unlocked by being pulled out; the width of the mattress on the bed frame was 83 cm. The height of the bed (from the floor to the mattress surface) at the beginning of the intervention was 68 cm. Participants could freely change the height of the bed and remove the bed railing. At the end of the intervention, the bed and beddings were reset to default settings. The care receiver was a healthy man in his 60 s, weighing 65 kg, was 175 cm tall, and acted as a dependent patient. To ensure that he faithfully represented a bedridden patient with contractures, communication difficulties, and no limb movement, he was instructed to provide no assistance to caregivers and to relax his muscles. In addition, the researcher explained the purpose of the study and the mechanism and features of the device to the care receiver beforehand, and the care receiver was confirmed to behave as a bedridden patient by lying on the device. We used a simulated—instead of a real—patient to exclude possible biases caused by patient variation and because this was a feasibility study, which aimed to evaluate caregiver load; however, it was important that caregivers provide the care to a real person so that their power or touch of hands for turning the care receiver would be different from that for a mannequin. The device used in the study, along with a personal computer on which a video on how to use it could be viewed, was installed in a conference room in the facility where the research participants worked one week before the study was conducted so that anyone could freely practice the device. In addition, on the day of the study, we provided participants with instructions regarding the nursing care procedure and how to operate the device at the bedside.Previous studies26, and lumbar intervertebral disc pressure is considered to be a physical quantity that directly indicates the force on these discs. However, since intradiscal pressure measurement requires the use of an invasive procedure of inserting a pressure sensor directly into the disc27, a pressure estimation method based on a biomedical model is currently used28. The upper limit of 3400 N was defined in the Work Practices Guide for Manual Lifting29. The guideline was based on the autopsy data of previous studies31 and has since been reinforced by epidemiologic studies and other sources32. Today, the criterion of 3400 N is a widely recognised standard, although significant differences by age and sex have been reported33. This criterion has also been used in studies of patient handling in the healthcare field, and it has been shown that the low back load on caregivers during patient handling, such as repositioning and transfers, is extremely high35.One of the causes of low back pain is irreversible damage to the lumbar intervertebral discs due to excessive force on the discs36, in the standing position, forward flexion of 20° causes disc loading. According to Andersson’s study38, as the flexion angle increases, lumbar intervertebral disc pressure also increases, and muscle activity stops at 45° of trunk flexion angle. Based on the estimated spinal compression forces, unloaded trunk flexion at a 45° angle causes compression forces to exceed 50 percent of the NIOSH threshold39. Furthermore, carrying a light load (100 N) significantly increases the magnitude of spinal compression during trunk flexion, exceeding 85 percent of the NIOSH threshold at a 45° trunk angle39. Based on these basic biological studies, a trunk flexion angle of 45° or more was defined as an extremely hazardous posture and a high risk of low back pain in the workplace41. Punnet42 conducted a case–control study based on this threshold and found an increased risk of low back pain in workers with a non-neutral posture of 45° or more. Studies evaluating the risk of low back pain for caregivers have also been conducted using a threshold based on trunk flexion45.Forward bending is one of the most common causes of load on the lumbar intervertebral discs. According to Nachemson’s studyTrunk flexion angle in this study48. The correction angles were calculated using linear regression between sensor data and the trunk flexion angle on several pictures obtained from the videos using the Kinovea software (Kinovea Inc). For easy and precise measurement, we attached the tracking markers on the acromion and greater trochanter of participants. To establish inter-rater reliability, angle measurements using Kinovea were performed by two independent testers who were not involved in this study. The inter-rater reliability of these two testers was 97.5%. We derived a regression equation using the sensor and some video images during the first intervention for each participant. Subsequently, using the regression equation, we computed the corrected angles, and the coincidence of angles between the corrected angles and the angles from video images was confirmed during the second intervention when the slope of the regression curves was 1.0. We confirmed the slopes of all regression curves and the coefficients of determination of all regression equations, and a well-fitted model was constructed.The primary outcome was the time it took for caregivers’ trunk flexion angle to exceed 45°. We defined the ‘trunk flexion angle’ as the angle between the vertical line and a straight line passing through the greater trochanter and shoulder. Trunk flexion angle was estimated using inertial measurement units . The sensor was a small (51.3 × 36 × 15 mm), wireless, battery-powered unit that measured and recorded acceleration  and angular velocity . A trunk postural data sensor was placed on each participant’s manubrium sterni to minimise the interference of the device during work activities. The device logged data at a rate of 5 samples/s. We also incorporated video analysis to enhance the quality of the trunk flexion angle data45, we established a trunk flexion angle of 45° as the threshold level of low back load to evaluate the efficacy of our novel assistive device. We categorised and calculated the time of flexion (time and %time), the average and maximum trunk flexion angle during the turning intervention, the frequency of trunk flexion maintenance at > 45°, and the maximum trunk flexion maintenance time at > 45°.Based on previous studiesWorking time was defined as the time (in seconds) required to perform a complete turning intervention.Before and after each intervention, participants reported subjective fatigue (body fatigue and low back fatigue), measured using a 100-point visual analogue scale (VAS) with 0 points indicating ‘no fatigue’, and 100 points indicating ‘unbearable fatigue’. In the analysis, the change (range from − 100 to 100) calculated by subtracting the post-intervention value from the pre-intervention value is used. Thus, if the change is negative, it indicates a decrease in subjective fatigue after the intervention compared to before the intervention., having both hands of the caregiver free for working , and comfort for care receivers , in comparison with the manual method by rating according to a seven-point Likert scale (from strongly agree to strongly disagree). Furthermore, participants rated items, including the tilt angle  and tilting speed, with additional comments.Upon the completion of both interventions, participants performed a comparative evaluation of the usability of the device via a survey. They evaluated the device’s usefulness in labour-saving Demographic data were analysed using descriptive statistics and are reported as means with percentages or frequencies for categorical variables. The comments that accompanied the usability scoring survey were classified according to similarity. After classification, the relevance of the classification was checked by an independent researcher. To evaluate whether the device was effective in reducing caregiver burden, we analysed continuous variables using linear mixed models, with intervention for repeated measures for each participant. Intervention, period, and intervention-by-period were treated as fixed effects, and participants were treated as random effects. We estimated the adjusted differences of variables by classifying the following three patterns:・There was a significant difference in intervention-by-period (regardless of the period); the adjusted difference was estimated using a model that included intervention, period, and intervention-by-period.・There was no significant difference in intervention-by-period, but there was a significant difference in period; the adjusted difference was estimated using a model that included intervention and period.・There was no significant difference in intervention-by-period or period; the adjusted difference was estimated using a model that included only the intervention.Analyses were performed using JMP® 15 . The level of statistical significance was set as p < 0.05.49 and a comparative study on device development50. Based on the results of the pre-test and these previous studies, we set the target sample size to 30.Based on a pre-test involving three experiments, the mean times (standard deviation [SD]) in which the trunk flexion angle exceeded 45° was 31.5 (30.0) s and 53.4 (2.2) s for the Device and Manual methods, respectively. We set the correlation during the study periods to zero to preserve the power of the test. As this study did not include actual patients, the sample size calculations did not violate any ethical issue. Assuming a power and type 1 error in a two-sided test of > 0.8 and < 0.05, respectively; the minimum estimated sample size required for comparison between the Device and Manual methods was 32. Moreover, we considered the findings of previous studies that evaluated low back loads by measuring trunk flexion angle using sensors in healthcare workersThe study was registered with the University Hospital Medical Information Network (registration number: UMIN000037746). All methods were carried out according to relevant guidelines and regulations.All participants provided oral and written informed consent before the experiment, and the experimental protocol was approved by Osaka University Clinical Research Review Committee .We allocated 30 participants to the interventions in this study; 15 participants were allocated to each of the two sequences. We included 28 participants in the final analysis after excluding two participants who did not report their demographic characteristics; in addition, one of these participants did not have the sensor fastened, causing an error in data collection, i.e., this participant met two exclusion criteria. Adverse events were not observed during this study. The summary of participants’ data is shown in Table All variables were analysed using linear mixed models with intervention for repeated measures for each participant, and least-square means (LSM) and 95% confidence intervals (CIs) are shown in Table p < 0.001). The adjusted differences in other parameters for trunk flexion were also significant, as was the time for extreme flexion.The adjusted LSM time for extreme trunk flexion (> 45°) was 77.1 s for the Device method and 103.5 s for the Manual method. Thus, the time for extreme flexion was 26.5 s shorter using the Device method than using the Manual method  (p < 0.001).The adjusted LSM working time was 263.6 s for the Device method and 198.0 s for the Manual method. Thus, the mean working time in the Device method was 65.6 s longer than that in the Manual method  (p < 0.001).The adjusted LSM body fatigue score was -6.6 for the Device method and 17.2 for the Manual method. Thus, the body fatigue score in the Device method was 23.8/100 points lower than that in the Manual method  (p = 0.148).The adjusted LSM low back fatigue score was 6.9 for the Device method and 17.3 for the Manual method. Thus, there was no significant difference between the Device and Manual methods in terms of the VAS score for subjective low back fatigue   with the use of the device compared with that in the manual group. The average and maximum trunk flexion angles of caregivers during patient care with the device were smaller than those observed with the manual method; moreover, the frequency of trunk flexion maintenance and the maximum flexion maintenance time was reduced with the device. Hence, the device was found to potentially reduce the low back load of caregivers. However, even with the use of the device, the trunk flexion angle of the caregiver is sometimes 45° or more. In addition, even if there are times when the flexion is less than 45°, the caregiver still suffers from a lumbar load, even with a slight forward tilt of 20°, as evidenced by Nachemson's study27. In other words, the new device has not completely eliminated the caregiver’s low back load. Indeed, it is clear that the current device reduces the low back load by decreasing the severe trunk flexion time; however, it is not clear at this time whether this prevents the development of low back pain. According to the review examining the effectiveness of assistive devices in preventing low back pain, there is no clear evidence to support the effectiveness of assistive devices in preventing low back pain51. This is partly because the magnitude of the load on the lumbar region is not only factor related to the onset of low back pain, and partly because the relationship of low back pain with the proper use of the device has not been clarified. In order to verify the effectiveness of this device in preventing low back pain in the future, it is necessary to conduct a long-term study on the use of the device after its introduction, and the resulting prevalence of low back pain.To measure our primary outcome of trunk flexion angle among caregivers during patient care, we analysed the trunk flexion angle categories using 45° as the threshold, based on previous studies 52 described the importance of the minimal clinically important difference (MCID) as “the smallest difference in scores in the domain of interest which patients perceive as beneficial”. In the results of VAS measurements of changes in chronic low back pain53 and acute pain54, the MCID ranged from 13 to 19 mm. Although the participant demographics were different from those in this study, we considered the difference of 26.4 in current study to be clinically significant. Therefore, the developed device effectively assisted caregivers during patient care by reducing subjective body fatigue levels. In addition, the change in subjective fatigue with the device method was negative, indicating less fatigue after the intervention than before. This trend was the same in periods 1 and 2 of the intervention  on the nursing staff who care for older patients with special needs. Developing an effective device for use in hospitals and nursing homes for turning and supporting bedridden patients will improve and maintain care quality. It is necessary to continue seeking multi-faceted solutions for reducing caregiver load and continuously develop this new system, despite the many challenges in the practical application of this device, such as safety concerns, high development cost, and usability.As the ageing populations in many countries continue to grow57. However, when our product is completed, it should be used on patients with contractures in a clinical setting to verify the safety of the devices. Second, it used a small sample size and convenience sampling. The number of valid answers to the questionnaire was small (28), and participants were recruited from only one facility. Therefore, detailed analyses, such as a stratified analysis based on age and years of experience, which may affect the usability and usefulness, were not performed. Therefore, our participants may not be representative of the broader nursing population, which limits the generalisability of our results. Finally, the device used in the study was set up in a conference room at the study facility in advance and was available for practice at will, but when the participants were asked on the day of the study, few had practised with the device and most had only watched a video of it. Lack of proficiency in using the device may have been related to longer working time and increased subjective fatigue. In the future, it will be necessary to assess the degree of proficiency of the participants and consider the effects on the evaluation.This study’s findings are meaningful, as it was conducted using an appropriate study design with randomised interventions. Furthermore, analyses were performed using a mixed model that enabled comparisons within each participant, thereby avoiding the influence of participant characteristics, such as the caregivers’ clinical experiences or sex. However, the study has some limitations. First, care receivers could not be evaluated, because only one care receiver was involved in the study to ensure a uniform level of task burden for all participants. Although caregivers are primary users, care receivers are also essential users of such assistive devices; hence, their opinions regarding comfort and safety must also be recognised. Furthermore, in this study, because a healthy person played the role of a bedridden patient, the condition of actual paralysis and contractures may not have been faithfully represented. Therefore, it cannot be denied that the caregiver’s evaluation of the device may change when using this bed to care for patients with contractures. Similar to our study, other studies have simulated healthy people when measuring the effectiveness of turning and lateral transfer devices in an experimental settingWe developed a novel device that was programmed to laterally turn (on the long axis of the body) and support a patient’s body while receiving care. The new assistive device reduced the low back load and subjective fatigue level in caregivers, and received a positive subjective evaluation.Supplementary Information.


Aiming at the problem of stability control of surrounding rock in the process of upward mining of inclined coal seam gob-side entry retaining, the gob-side entry retaining of 5449 working face in Beipingdong coal mine is taken as the research background. Based on the study of theoretical model of gob-side entry retaining, the law of overburden movement and surrounding rock deformation in the process of gob-side entry retaining is obtained through similar simulation experiment and numerical simulation analysis, and the optimal optimization scheme of supporting structure is put forward. The main research results are as follows: in the process of coal seam mining, the center of gravity of the overlying strata on the whole working face will shift to the side of the goaf, and the supporting structure composed of roadside filling body and bolt effectively inhibits the deformation of the roadway, in which the roadside filling body plays a key role; the top bolt bears greater stress than the side bolt in the process of mining, so the top bolt mainly maintains the stability of the roadway, and the side bolt plays the role of strengthening the side and preventing the extrusion. The width of the roadside filling body should be about 2.5 m, and the plastic failure range can be well controlled under this width. Chinese scholars have carried out a large number of studies on the application of this technology from different angles. He5 team has established a mechanical model of "surrounding rock structure—roadway side support body" under different roof positions, and based on this model, proposed the surrounding rock control technologies of roof cutting and pressure relief, such as concentrated presplitting blasting, constant resistance large deformation anchor cable, and dense single pillars beside the roadway, which laid a foundation for the successful implementation of roof cutting and pressure relief along the goaf. Zhu6 and others established the reliability analysis model of the support structure for gob side entry retaining in fully mechanized top coal caving according to the large deformation of surrounding rock and many random variables in the mechanical parameters of surrounding rock, and obtained the calculation formula for the reliability of the support structure. Zhang10 used "three high" bolts + anchor cable beam for initial support, anchor cable beam + grouting for advanced reinforcement, I-shaped steel beam + articulated roof beam for auxiliary support, paste material pumping and filling to construct the wall in the gob side entry retaining of high ground pressure soft rock roadway in a 1000 m deep well, effectively controlling the severe deformation of the side and roof of the gob side entry retaining in deep. Hua13 conducted an in-depth study on the deformation and failure mechanism of surrounding rock of gob side entry under the condition of deep well composite roof, and proposed the support countermeasures and technical schemes of "step by step strengthening at all stages" and "strengthening surface protection" for gob side entry under the condition of deep well composite roof, which have achieved good results in production. At the same time, domestic and foreign experts have also made fruitful achievements in the aspects of dynamic disaster15, roadway side support17, and roadway surrounding rock deformation control20 in the process of gob side entry retaining.The technology of retaining lanes along goaf has brought more favorable technical advantages and economic benefits to coal mining in China, and is a major breakthrough in the maintenance of lanes without coal pillars23, but there are still many technical problems in this technology under the conditions of complex geological conditions, high dynamic pressure in the deep and other conditions. Therefore, this paper takes the working face of gob side entry retaining for upward mining of an inclined coal seam in Beipingdong Coal Mine as the background, studies the failure law of surrounding rock in gob side entry retaining for upward mining of inclined coal seams, and optimizes the support structure based on the deformation and failure law of surrounding rock, provide ideas for safe mining of goaf side entry in similar inclined coal seams.It can be seen that gob side entry retaining is still a safe and efficient mining technology in the future coal miningThe 5449 working face of Beipingdong Coal Mine is located in the wusi mining area. The east side of the working face is close to the 5447 working face, and the west side is the non mining area; The ground is high mountains without buildings. The 5449 air tunnel mainly adopts four coal seams with good quality. The coal seam thickness is 1.0–1.4 m, the average coal thickness is 1.2 m, the coal seam dip angle is about 29°, the working face slope is large, the dip angle range is 12°–55°, the average is 25°. The direct top of Coal Seam 4 in the working face is dark gray black sandy mudstone, containing plant fossils and iron nodules, with a thickness of 0–10.8 m and an average of 5.4 m; The direct bottom of the working face is gray black sandy mudstone, containing plant root fossils, with a thickness of 0.9–4.24 m and an average of 1.73 m; The main roof of the working face is 4.17 m fine sandstone. The histogram of the top and bottom of the coal seam is shown in Fig. 2. The roadway section and anchor bolt layout are shown in Fig. The section of 5449 air roadway in Beipingdong Coal Mine is trapezoidal, the left side height of the roadway is 2000 mm, the right side height is 2400 mm, the roadway width is 2500 mm, and the net section area of the entire roadway is 4.4mThrough the similar simulation experiment, the dynamic evolution law of overburden collapse under the technical condition of gob side entry retaining in 5449 working face of Beipingdong Coal Mine, as well as the influence of working face recovery on the deformation of roadway surrounding rock are analyzed, revealing the role of bolt in restraining roadway deformation during mining, thus obtaining the bolt that plays a key role in roadway support, and further optimizing the roadway bolt support layout.The experimental simulation platform device can accommodate the model size of 2000 mm × 500 cm × 1400 mm. Based on the size of the model experimental device and the geological production conditions on site, a physical similarity model (length × width × height = 1500 mm × 500 mm × 1200 mm) was laid along the inclination of the working face. The similarity ratio between each physical quantity should satisfy the following relationship: Geometric similarity ratio In the model, river sand, lime, gypsum and water are uniformly mixed as similar materials according to the specified proportion for rock layers of different lithology. Timber strips are used to simulate coal seam mining. The proportion of materials used for similar material models is strictly in accordance with Table During the experiment, five monitoring lines are arranged at 1 m, 2 m, 3 m, 4 m and 5 m above the coal seam, as shown in Fig. The process of coal mining is often accompanied by the collapse of the overlying strata. As one of the important characteristic parameters, the displacement of the strata is closely related to the stability of the surrounding rock of the roadway. Therefore, the relationship between the five monitoring lines above the roadway and mining and the collapse process of the overlying strata are plotted, as shown in Figs. (1) At the initial stage when the mining face reaches 6 m and 7 m, the roof subsidence starts to appear above the roadway, but the subsidence is small. The subsidence of the coal roof is greater than that of the roadway roof, and the maximum displacement reaches 182.1 mm at 2 m above the working face; due to the influence of mining, the strata above the side of the goaf are separated from each other. The separation range extends to 16 m above the goaf. The overlying strata have a large area of overhanging roof and are caving with mining. The filling body beside the roadway needs to bear the vertical stress of some overhanging roof strata. Therefore, a small part of the rock mass is squeezed off on the surface of the filling body beside the roadway.(2) The collapse of all low level rock strata at the goaf side leads to a sharp increase in the subsidence of the overlying rock strata, which makes the stress concentrated at the goaf side, causing a new separation of high level rock strata. The collapsed rock is gradually compacted to support the high level rock strata, slightly slowing down the trend of bending and sinking of the high level rock strata. The maximum subsidence is located 5 m above the right side of the roadway, and the maximum subsidence reaches 359.6 mm; the crack at the upper right side of the roadway extends to the upper part of the roadway, and the entire overlying rock stratum breaks along the crack and settles toward the goaf side. As the only support structure, the roadway side filling body needs to bear the load of a larger area of suspended roof rock, so the roadway side filling body is severely squeezed and destroyed and loses its support capacity.In the process of similar simulation experiment, the stress of anchor bolt is monitored in real time, and the distribution of anchor bolt and sensor is shown in Fig. During the experiment, a section of anchor bolt is set according to the layout of the anchor bolt in the air roadway of the 5449 working face of Beipingdong Coal Mine to monitor the axial stress of the anchor bolt during the mining process. The axial stress of the anchor bolt during the experiment is shown in Fig. As shown in Fig. From this analysis, it can be seen that the roof bolts play a key role in restraining the roadway deformation during the mining process of the gob side roadway, especially the bolts on both sides of the roadway bear a large stress, and the side bolts mainly play a role in strengthening the stability of the side to prevent the occurrence of wall collapse.According to the law of rock stratum destruction in the similar simulation experiment, in the process of gob side entry retaining mining, the roadway side support is the main bearing structure, which plays a vital role in the stability of the roadway. In the experiment, the width of the roadway side fill was taken as a fixed value for the experiment. In this section, the roadway side support width was studied as a key variable, and the FLAC3D simulation software was used to simulate the distribution of plastic zones under different roadway side supports, the purpose is to simulate and determine the best filling body width beside the roadway.Size of numerical simulation model: length × high × Width = 100 m × 50 m × 50 m. For the numerical model of 50 m, the average thickness of the coal seam is 1.2 m, the buried depth of the coal seam is 600 m, the unit weight of rock is 25KN/m3, the average density of rock stratum is 2700 kg/m3, and the initial vertical stress applied on the top of the model P = 9.0Mpa. The whole model is divided into 970,587 units and 174,743 nodes. The FLAC3D numerical simulation model is established as shown in Fig. In order to conform to the actual situation on site as much as possible, the Mohr Coulomb model is used in the calculation process. In the simulation process, it is assumed that the model is under hydrostatic pressure, that is, the set side pressure coefficient is 1. The displacement boundary conditions are applied to the front, rear, left, right and bottom of the model, while the stress boundary conditions are applied to the top. In this way, the lower boundary, front and rear boundaries, and left and right boundaries of the model are fixed. The whole model is under gravity acceleration, and a uniform load equal to the overburden weight is applied to the top.The distribution characteristics of surrounding rock plastic zone is the key index to indicate the stability control of surrounding rock. The formation of surrounding rock plastic zone of roadway is the result of multiple factors. The shape of plastic zone is mainly affected by the stress state of surrounding rock. Therefore, this section studies the relationship between the width of the filling body and the distribution of the plastic zone, and studies the stability of the roadway from the perspective of the plastic zone. The cloud distribution diagram of the plastic division after the simulation calculation is shown in Fig. When the width of the filling body is 0.5 m and 1 m, the strength of the filling body is far from the requirement of supporting the roof due to its insufficient width, so the plastic zone is large at this time, the plastic failure zone is very developed and interconnected, the filling body beside the roadway has both tensile failure and shear failure, and the surrounding rock of the roadway is severely damaged.When the filling body width is increased to 1.5 m and 2 m, the plastic area is much less than 0.5 m and 1 m. The plastic failure at the roadway roof begins to shift to the filling body roof and decreases. The roadway roof rock is mainly shear failure. When the filling body width is increased to 2.5 m and 3 m, the plastic zone area is significantly reduced, and the phenomenon that the plastic zones are interconnected can be better controlled.It can be seen from Fig. The comparative analysis of Fig. In the process of gob side entry retaining, the gravity center of the overlying strata of the whole working face will shift to the side of the goaf. The support structure composed of the roadway side filling body and anchor rod effectively inhibits the deformation of the roadway, and the roadway side filling body plays a key role.In the bolt structure, the top bolt bears a large stress relative to the side bolt in the mining process, so the top bolt mainly maintains the stability of the roadway, and the side bolt strengthens the side and prevents the side from being squeezed.The range of plastic zone of surrounding rock of roadway under different widths is simulated by numerical simulation, and the optimal filling body width should be about 2.5 m from the perspective of roadway stability, roadway maintenance difficulty and economy, so that the plastic failure range under the width can be well controlled.