Core components and strategies for suicide and risk management protocols in mental health research: a scoping review
Bibliographic record
Abstract
BACKGROUND: Suicide and risk management protocols in mental health research aim to ensure patient safety, provide vital information on how to assess suicidal ideation, manage risk, and respond to unexpected and expected situations. However, there is a lack of literature that identifies specific components and strategies to include in suicide and risk management protocols (SRMPs) for mental health research. The goal of this scoping review was to review academic and grey literature to determine core components and associated strategies, which can be used to inform SRMPs in mental health research. METHODS AND ANALYSIS: The methodological framework outlined by Arksey and O'Malley was used for this scoping review. The search strategy, conducted by a medical librarian, was multidisciplinary and included seven databases. Two reviewers independently assessed eligibility criteria in each document and used a standardized charting form to extract relevant data. The extracted data were then examined using qualitative content analysis. Specifically, summative content analysis was used to identify the core components and strategies used in SRMPs. The data synthesis process was iterative. RESULTS: This review included 36 documents, specifically 22 peer-reviewed articles and 14 documents from the grey literature. Five core components of SRMPs emerged from the reviewed literature including: training; educational resources for research staff; educational resources for research participants; risk assessment and management strategies; and clinical and research oversight. Potentials strategies for risk mitigation within each of the core components are outlined. CONCLUSIONS: The five core components and associated strategies for inclusion in SRMPs will assist mental health researchers in conducting research safely and rigorously. Findings can inform the development of SRMPs and how to tailor them across various research contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".