Suicide Prevention
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to describe the pilot expansion of a proactive suicide risk-screening program, initially designed for physicians, to nurses. BACKGROUND: The Healer Education, Assessment and Referral (HEAR) program detects at-risk physicians and facilitates referral to mental healthcare. Nothing similar has been available for at-risk nurses. Local nurse suicides served as the catalyst to extend the HEAR program to nurses. METHODS: Education, outreach, and an encrypted, online, anonymous, proactive risk screening were conducted to identify and refer nurses with depression and suicide risk. RESULTS: During the 1st 6 months of the program, 172 of 2475 (7%) nurses completed questionnaires; 74 (43%) were rated as high risk, and another 98 (55%) as moderate risk; 12 (7%) reported current active thoughts or actions of self-harm, and 19 (11%) reported previous suicide attempts. Forty-four (26%) received in-person or verbal counseling, and 17 accepted referral for continued treatment. CONCLUSIONS: An encrypted, anonymous, proactive risk screening is effective at identifying nurses at risk and referring them to mental healthcare.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.018 |
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".