The mental health of police personnel: what we know & what we need to know and do (CACP-MHCC Conference 13–15 February 2017)
Bibliographic record
Abstract
The issue of mental health and wellness has gained greater attention in society as a whole in the past decade. The Canadian Association of Chiefs of Police (CACP) has had this topic on its radar for even longer, and continued this sustained emphasis at the 13–15 February 2017 conference entitled “The Mental Health of Police Personnel: What We Know & What We Need to Know and Do”. The dynamic and fast-paced conference was organized by the CACP and moderated by Norman E. Taylor. It brought together 222 delegates and speakers representing the broad sectors of policing, mental health and research, with equal numbers of men and women, at the Hilton Lac-Leamy in Gatineau, Quebec. Collaborating in this initiative were the Mental Health Commission of Canada (MHCC), Canadian Police Association (CPA), the Canadian Association of Police Governance (CAPG), the CACP Research Foundation (CACP-RF), the Canadian Institute for Public Safety Research and Treatment (CIPSRT), and Public Safety Canada (PSC). This paper provides a comprehensive report on the proceedings as submitted, and has been approved for publication in this Journal by the board of directors of the CACP.Some speakers provided the CACP with permission to post the visual aids they used for their presentations. These are available on the CACP website at https://www.dropbox.com/sh/pfjkme79redafo/AADGWJPod7K2jOJzlmwnFIsEa?dl=0
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.054 | 0.016 |
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".