MétaCan
Menu
Back to cohort

Sharing knowledge on implementing mental health and wellbeing projects for veterans and first responders

2025· article· en· W4406860250 on OpenAlexaff
Cindy Woods, Sally Fitzpatrick, Sue Lukersmith

Bibliographic record

VenueComprehensive Psychiatry · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsWestern UniversityCanadian Mental Health AssociationMcMaster University
FundersMovember Foundation
KeywordsMental healthPsychologyKnowledge sharingKnowledge managementApplied psychologyPsychiatryComputer science

Abstract

fetched live from OpenAlex

PURPOSE: The aim of this study was to describe the knowledge to action and knowledge transfer approaches used in an international mental health research funding program and its outcomes. A key goal of the evaluation was to maximise organisational learning and knowledge sharing to inform future implementation projects. METHODS: A series of interactive knowledge sharing workshops focused on five key themes: peer support; psychoeducation; the involvement of family, friends, and significant others; retreat, residential, or group-based programs; and organisational change. Qualitative descriptive analysis was used to code, summarise and describe themes. FINDINGS: Key learnings that influenced the success of mental health initiatives include building relationships across all organisational levels, involving Veterans and First Responders with lived experience in the design and implementation process, and understanding the unique workplace culture and operations. CONCLUSION: Our findings highlight the need for collaborative, informed approaches tailored to the culture, organisation and mental health support needs of Veteran and First Responder. These insights enhance understanding of the factors that impact the successful implementation of mental health prevention and support programs for those exposed to work-related trauma.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.441
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.343
GPT teacher head0.607
Teacher spread0.264 · 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 designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueComprehensive PsychiatrySame topicHealth Policy Implementation ScienceFrench-language works237,207