Factors influencing suicide risk assessment clinical practice: protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: Every year, suicide accounts for nearly 800 000 deaths worldwide. Appropriate risk assessment and intervention are imperative since evidence demonstrates that a large proportion of those who die by suicide visit health professionals prior to their death. Much previous research has focused on identifying patient-level risk factors that can improve the risk assessment process through scales and algorithms. However, the best practice guidelines emphasise the importance of clinical interviews and prioritise the clinician's final judgement. The purpose of this review is to (1) understand the clinician and organisational level barriers and facilitators that influence a clinician's assessment of suicide risk, (2) identify the types of biases that exist within this process and (3) list any evidence-based training protocols and educational initiatives to aid (or support) clinicians with this process. METHODS AND ANALYSIS: This scoping review protocol uses the Arksey and O'Malley framework, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines for scoping reviews. Literature will be identified using a multidatabase search strategy developed in consultation with a medical librarian. The proposed screening process consists of a title and abstract scan, followed by a full-text review by two reviewers to determine the eligibility of articles. Studies outlining any factors that affect a clinician's suicide risk assessment process, ranging from individual experience and behaviours to organisational level influences, will be included. A tabular synthesis of the general study details will be provided, as well as a narrative synthesis of the extracted data, organised into themes using the Situated Clinical Decision-Making framework. ETHICS AND DISSEMINATION: Ethical approval is not required for this review. Results will be translated into educational materials and presentations for dissemination to appropriate knowledge users. Knowledge outputs will also include academic presentations at relevant conferences, and a published, peer-reviewed journal article.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.111 | 0.126 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.124 | 0.023 |
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 source (direct Gemma or distilled Codex), 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".