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Record W4408613081 · doi:10.1177/10482911251326648

Exploring Canadian Public Safety Communicator Mental Health Help-Seeking Behaviors

2025· article· en· W4408613081 on OpenAlexafffundabout
Emily Howe, Stephen Czarnuch, Rosemary Ricciardelli, N. Leduc

Bibliographic record

VenueNEW SOLUTIONS A Journal of Environmental and Occupational Health Policy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsMemorial University of Newfoundland
FundersMitacs
KeywordsDenialMental healthPsychological interventionPsychologyStigma (botany)FeelingPublic healthPublic relationsSocial psychologyMedicineNursingPsychiatryPolitical sciencePsychotherapist

Abstract

fetched live from OpenAlex

Public safety communicators are the first line of support for members of the public-facing emergency situations. Consequently, communicators are exposed to potentially psychologically traumatic events (PPTE) which are associated with an increase in the prevalence of mental health concerns. For communicators, PPTE exposure and the subsequent negative mental health consequences are exacerbated by low engagement in mental health help-seeking behavior. We surveyed (n = 361) Canadian public safety communicators, asking "What do you think stops people from getting help for their mental health" to identify, contextualize, and provide considerations about contributors to the lack of mental health help-seeking among communicators. Emergent theme analysis reveals 7 factors that circumvent help-seeking: access barriers; self-denial; consequences of seeking help; lack of knowledge; personal feelings; stigma and culture; and support. Discovering hindrances to help-seeking identifies how factors contribute to communications employee wellness and supports the creation of effective interventions and policy implementations to support communicator mental health.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.497

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.006
Science and technology studies0.0100.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.118
GPT teacher head0.380
Teacher spread0.261 · 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 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

Citations4
Published2025
Admission routes3
Has abstractyes

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Same venueNEW SOLUTIONS A Journal of Environmental and Occupational Health PolicySame topicDisaster Management and ResilienceFrench-language works237,207