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Record W4321607582 · doi:10.35502/jcswb.295

Mental Health Secretariat: Collaboration for public safety personnel (PSP) mental health in Ontario

2023· article· en· W4321607582 on OpenAlexaffvenueabout
Beth Milliard, Robert Chrismas

Bibliographic record

VenueJournal of Community Safety and Well-Being · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsCanadian Institute for Public Safety Research and TreatmentUniversity of Regina
Fundersnot available
KeywordsMental healthMandateChristian ministryAction planPublic healthPublic relationsMedicinePsychologyNursingPolitical scienceManagementPsychiatry

Abstract

fetched live from OpenAlex

Mental health issues, and more specifically suicide, within the policing community have been a growing concern in recent years. In 2018 alone, there were nine suicides among active and retired police officers in the province of Ontario. At the time, nine suicides in one year were shocking and began to raise focused awareness of mental health challenges facing the profession. In 2021, the Ontario Ministry of the Solicitor General created Mental Health Collaborative Tables comprised of key stakeholders, subject matter experts, public safety personnel (PSP) with lived experience, mental health clinicians, and researchers. The Mental Health Secretariat (MHS) is responsible for supporting the tables. The MHS is accountable to the Deputy Solicitor General and has a mandate to provide a provincial action plan to address mental health issues among PSP. This article explains key observations regarding Ontario’s innovative approach to improving mental health supports for PSP and describes the perspective offered by Karen Prokopec, Manager, MHS at Ontario Ministry of the Solicitor General, and her colleague, Zarsanga Popal, Senior Performance Measurement and Evaluation Specialist with the MHS, on the establishment of the MHS.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.404
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes3
Has abstractyes

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