Suicide Prevention through Shared Information
Bibliographic record
Abstract
ABSTRACT ObjectivesAlthough mental health clinicians are in the best position to assess a person’s risk for suicide, people at imminent risk may first seek the help of crisis workers, the police, hospital staff, or family members. The present project will use international (UK Biobank), provincial, and local crisis line data to elucidate imminent risks for suicide. An interdisciplinary team will discuss whether a common protocol for handling acute cases is warranted. The possibility of sharing a minimal set of information across services will also be discussed. ApproachThe conceptual framework is that suicide is a probabilistic outcome of risks (comprised of inherited traits, habits, and environmental stressors) that can be put in temporal order as distal, proximate, and immediate antecedents. Local, provincial, and international data will be mined for risks corresponding to each epoch. The evidence will be assessed by an interdisciplinary team composed of patient advocates, psychiatrists, the police service, community health workers and academic researchers with the objective of reaching an agreement on a common protocol for suicidality assessment. The possibility of sharing a minimal dataset that is relevant to saving the lives of people at imminent risk of suicide will be explored. Finally, the efficacy of coordinated care across services will be evaluated by comparing suicide and self-harm rates will be assessed by comparing suicide and self-harm rates before and after the adoption of the protocol. ResultsAn interdisciplinary team has been formed and funding for the project is being sought. An application for data access to the UK Biobank received preliminary approval and is being evaluated by the scientific committee. Applications for access to provincial administrative data as well as telephone crisis line data for the last 10 years are being prepared. ConclusionRoutinely collected administrative data is a resource for the collective decision-making of an interdisciplinary team of experts and patient advocates. The ability of critical information to flow across organizational boundaries may be an important tool in suicide prevention. Dialogues regarding the ethical dilemma between potentially saving lives and potentially breaking privacy may need to happen.
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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 | 0.031 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.006 |
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".