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Suicide is a leading cause of death among Autistic adults globally. Autistic people are up to six times more likely to die by suicide than people in the general population. Research highlights a lack of appropriate support for Autistic individuals experiencing suicidal thoughts and behaviours.
A scoping review will be conducted to map available literature on Suicide Prevention Interventions and Supports used with the Autistic community. This scoping review will use the methodological guidelines set out by the Joanna Briggs Institute Manual for Evidence Synthesis. The searches will be conducted in January 2025. The following electronic databases will be searched; PubMed, CINAHL Ultimate, PsycINFO and EMBASE, as well as the reference lists of included articles and grey literature (including conference abstracts, PhD theses, grey literature databases and preprints). The search strategy will be used to identify literature with an aim of preventing suicide in Autistic individuals. Only literature published in English will be included. Two reviewers will independently screen all literature based on predetermined inclusion and exclusion criteria. Data extraction will be piloted by two reviewers and continued by one reviewer. The extracted data will be checked for accuracy by a second reviewer. Any disagreements that arise between the reviewers will be resolved through discussion or with a third reviewer. A narrative summary of findings will be conducted. Results will be reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Review statement.
Ethics approval is not required for this study as it is protocol for a review of published literature and does not involve human participants or private data. Findings will be disseminated through professional networks, conference presentations and publication in a scientific journal.
This protocol has been registered on the Open Science Framework (https://osf.io/bpxnf/overview).
This study examined associations between pregnancy-related fear and stress, occupational exposures, and workplace modifications among pregnant registered nurses in the United States engaged in direct patient care.
A cross-sectional design was used with data collected via an online survey between November 2021 and April 2022. Participants (n = 358) were recruited through social media and listservs. Log-binomial regression models, adjusted for age and parity, estimated prevalence ratios and confidence intervals for associations between occupational exposures and workplace modifications with prevalence of pregnancy-related stress at work and fear of pregnancy or infant complications. Stress, a non-specific physical/psychosocial response to demands, and fear, an emotional response to perceived threat, functioned as distinct constructs.
Emotional and physical environmental hazards were associated with increased prevalence of stress. Emotional and environmental hazards, as well as physical movement, administering antineoplastic medications, infectious disease transmission and scans, were associated with increased prevalence of fear. Each additional occupational exposure increased prevalence of stress by 4% and fear by 12%. Nurses also mitigated risks by implementing workplace modifications. Stress was associated with changing work schedules, while fear was statistically significantly associated with taking extra infection precautions and seeking assistance for CPR.
Findings highlight the need for interventions that address modifiable occupational hazards and improve access to modifications that reduce stress and fear among pregnant nurses.
Strengthening workplace protections could reduce occupational stress, improve nurse retention and enhance patient care quality.
Pregnant nurses face significant occupational hazards, yet limited research has examined their psychosocial effects and mitigation strategies. This study identified key exposures associated with increased stress and fear and showed that workplace modifications varied by stress/fear levels and pregnancy trimester, informing policies to better protect pregnant nurses.
Authors adhered to the STROBE checklist for cross-sectional studies.
This study did not include patient or public involvement in its design, conduct or reporting.
Suicide is a significant public health issue worldwide. Many deaths by suicide occur in moments of crisis. Therefore, interventions which support individuals to manage moments of acute distress are needed. Safety Planning Interventions (SPI) are a group of brief interventions which aim to reduce imminent risk of suicide through the collaborative creation of a written set of coping strategies a person can use when suicidal ideation and/or urges occur. A number of studies, including systematic reviews, have supported the efficacy of SPIs in reducing suicidal behaviour, and sometimes ideation. However, there is notable heterogeneity in SPI effectiveness research. Our review aims to synthesise and critically examine the methodological characteristics of research on SPI effectiveness and to provide recommendations for the reporting of future research.
A predetermined search strategy will be used to search six electronic databases. Eligible studies will examine the effectiveness of SPIs for suicidality in adults aged 18+. There will be no restrictions to inclusion based on study design, study setting and participant characteristics. Two independent reviewers will perform study selection, data extraction and quality assessment. Disagreements between reviewers will be resolved by a third reviewer. Data gathered will include study design, participant characteristics, study setting, type of SPI delivered, theoretical approach used to guide research, outcomes measured and results reported. A narrative synthesis of the methodological characteristics of the included studies will be provided. Recommendations for the development and reporting of future research will be provided. Reporting of the review will be informed by Preferred Reporting Items for Systematic Review and Meta-Analysis guidance.
Ethical approval is not required as no original data will be collected. Findings will be disseminated through peer-reviewed publications and conference presentations.
This protocol has been registered on Prospero (registration ID CRD42025641027).
To predict nurses' turnover intention using machine learning techniques and identify the most influential psychosocial, organisational and demographic predictors across three countries.
A cross-sectional, multinational survey design.
Data were collected from 1625 nurses in the United States, Türkiye and Malta between June and September 2023 via an online survey. Twenty variables were assessed, including job satisfaction, psychological safety, depression, presenteeism, person-group fit and work engagement. Turnover intention was transformed into a binary variable using unsupervised machine learning (k-means clustering). Six supervised algorithms—logistic regression, random forest, XGBoost, decision tree, support vector machine and artificial neural networks—were employed. Model performance was evaluated using accuracy, precision, recall, F1 score and Area Under the Curve (AUC). Feature importance was examined using logistic regression (coefficients), XGBoost (gain) and random forest (mean decrease accuracy).
Logistic regression achieved the best predictive performance (accuracy = 0.829, f1 = 0.851, AUC = 0.890) followed closely by support vector machine (polynomial kernel) (accuracy = 0.805, f1 0.830, AUC = 0.864) and random forest (accuracy = 0.791, f1 = 0.820, AUC = 0.859). In the feature importance analysis, job satisfaction consistently emerged as the most influential predictor across all models. Other key predictors identified in the logistic regression model included country (USA), work experience (6–10 years), depression and psychological safety. XGBoost and random forest additionally emphasised the roles of work engagement, group-level authenticity and person–group fit. Job-stress-related presenteeism was uniquely significant in XGBoost, while depression ranked among the top predictors in both logistic regression and random forest models.
Machine learning can effectively predict turnover intention using multidimensional predictors. This methodology can support data-driven decision-making in clinical retention strategies.
This study provides a data-driven framework to identify nurses at risk of turnover. By integrating machine learning into workforce planning, healthcare leaders can develop targeted, evidence-based strategies to enhance retention and improve organisational stability.
This study adhered to STROBE reporting guideline.
This study did not include patient or public involvement in its design, conduct or reporting.
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