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Record W7084035573 · doi:10.2196/preprints.75285

Developing Recommendations to Improve Crisis Line Supports for Public Safety Personnel in Canada: Protocol for a Multimethod National Study (Preprint)

2025· article· en· W7084035573 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomics of Agriculture and Food Markets
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthCrisis interventionPublic healthService (business)Protocol (science)Occupational safety and healthMental health servicePhase (matter)Mental distress

Abstract

fetched live from OpenAlex

BACKGROUND Public Safety Personnel (PSP) in Canada experience disproportionately high rates of mental distress and suicidal thoughts and behaviors. PSP mental health is a critical public health issue with far-reaching implications for both individual well-being and the effectiveness of emergency response systems. Crisis lines are an evidence-based public health intervention; however, knowledge gaps remain regarding PSP crisis line use, barriers to accessing services, and the appropriateness of crisis line service models for meeting PSP mental health needs. OBJECTIVE This study aims to address these knowledge gaps using a participatory approach to better understand the crisis line needs and preferences of PSP communities. We also aim to apply our learnings and co-design actionable recommendations for crisis line service improvements and to support PSP who may wish to contact a crisis line. METHODS This Canada-wide study uses multiple methods across three iterative phases. Phase 1 involves community engagement with PSP to better understand their crisis needs and existing supports. Instrumental to our engagement ethic is the formation of a co-researcher group, composed of PSP with lived experience, who will guide the research process. We will review deidentified crisis line interactions to identify patterns in service use and call outcomes to identify possible points of intervention to enhance service efficacy. We will launch a national web-based anonymous survey to understand the crisis line needs, barriers, and preferences of PSP. Phase 2 focuses on deepening our understanding of PSP experiences with crisis lines through in-depth interviews with those who have accessed or thought about accessing crisis lines and those without crisis line experience who wish to share their views. We will conduct focus groups with crisis sector staff to learn about desired training and resources for improving service delivery to PSP. Phase 3 focuses on developing and conducting co-design workshops to generate evidence-based recommendations with PSP, crisis line responders, researchers, and clinicians. Collaborating across sectors will allow us to codevelop feasible strategies for improving crisis line services to better meet the needs of PSP in crisis who may be inclined to access crisis lines for support. RESULTS As of December 2024, the crisis line dataset has been identified and study recruitment for the national survey was completed. Data collection for all other research activities is expected to conclude by May 2025. We anticipate that study findings will be available by the end of 2025. CONCLUSIONS By identifying barriers to crisis line use and codeveloping solutions, this research will inform policy, service design, and training to enhance services. Ensuring PSP can access crisis line supports that are equitable, evidence-based, and integrated within mental health care systems is crucial to fostering a resilient public safety workforce and emergency response capacities at a societal level. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/75285

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)0.058
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.575
Threshold uncertainty score0.845

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0580.051
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0050.006
Bibliometrics0.0050.007
Science and technology studies0.0130.003
Scholarly communication0.0060.004
Open science0.0050.005
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0940.012

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 head0.074
GPT teacher head0.323
Teacher spread0.250 · 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 designNot applicable
Domainnot available
GenreProtocol

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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