Exploration of Protective and Risk Factors for Suicide Using Multiple Approaches
Bibliographic record
Abstract
Abstract Background In Canada, approximately 12 people die by suicide every day and recent trends have been showing increases in the occurrence of suicidal behaviour. There are various protective and risk factors associated with suicide. Risk factors can include lack of access to mental health services, and lack of social support. Protective factors can include social support, and connectedness with family and supports. These factors can be interconnected. Thus, it is important to explore the complexity of protective and risk factors for suicide. Methods We began our investigation by conducting a systematic review to assess the inclusion of family-based recommendations in national suicide guidelines. We then conducted a cross-sectional investigation looking at the influence of perceived social support on the association between stressful life events and suicidal behaviour. We then chose to implement a pilot study that looked at the Brief Intervention and Contact program, an evidenced based suicide intervention rooted in social support. We ended our investigation by exploring the patient perspective in suicide risk and assessed the association between patient important goals in Opioid Use Disorder treatment and suicidal ideation. Results Every national suicide guideline had some form of family-based recommendation, but limited implementation plans. Perceived social support did not influence the association between stressful life events and suicidal behaviour. The pilot study found reduced occurrence of adverse primary outcomes in the intervention arm. Finally, odds of having suicidal ideation were higher if participants with Opioid Use Disorder reported pain management or avoidance of illicit drugs as a goal, but lower if they reported wanting to stop treatment as a goal. Conclusion Future studies should continue to explore the complexities of protective and risk factors for suicide, while also focusing on the development and implementation of evidence-based suicide mitigation strategies.
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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 | 0.033 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.019 |
| Bibliometrics | 0.025 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".