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Record W4414267707 · doi:10.1080/15614263.2025.2558137

Barriers and facilitators to the implementation of peer support programs among public safety personnel: a scoping review

2025· review· en· W4414267707 on OpenAlexafffund
Véronique Lauzon, Maxime Paquet, Andrée-Ann Deschênes

Bibliographic record

VenuePolice Practice and Research · 2025
Typereview
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversité du Québec à Trois-RivièresUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of CanadaFonds de Recherche du Québec-Société et CultureFonds de recherche du Québec
KeywordsPeer supportPeer reviewQualitative researchSocial support

Abstract

fetched live from OpenAlex

While peer support programs have gained popularity as tools to mitigate stress among public safety personnel, there is a gap in research exploring the factors that contribute to the success of these programs. This scoping review synthesizes findings from international research on peer support programs for public safety personnel, using the PRISMA-ScR guidelines. Thirty-six documents met the eligibility criteria for this review. The results highlight ten themes describing the main barriers and facilitators to the implementation of peer support programs. These include concerns regarding the culture surrounding mental health, confidentiality, role delimitation, clarity of the program’s mission, tangible endorsement by administrators and stakeholders, selection, training and supervision of peer supporters, delivery format, and higher-level governance. By outlining the frequently stated components that may foster or hinder peer support programs, this study provides public safety organizations with insight regarding program design and policy making.

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 imitation

Not 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.

metaresearch head score (Codex)0.049
metaresearch head score (Gemma)0.146
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.049
Threshold uncertainty score0.261

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.146
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0160.015
Science and technology studies0.0020.002
Scholarly communication0.0060.004
Open science0.0030.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.183
GPT teacher head0.613
Teacher spread0.430 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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 routes2
Has abstractyes

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