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Record W4415142362 · doi:10.1080/15564886.2025.2568659

“I Wasn’t Alone”: Exploring Relational Dynamics Among Public Safety Service Users Undergoing Inpatient Mental Health Treatment in Canada

2025· article· en· W4415142362 on OpenAlexaffabout
Emma Vester, Krystle Martin, Matthew S. Johnston, Rosemary Ricciardelli

Bibliographic record

VenueVictims & Offenders · 2025
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsMemorial University of NewfoundlandOntario Tech University
Fundersnot available
KeywordsMental healthMental health serviceService (business)Suicide preventionOccupational safety and healthHuman factors and ergonomicsPoison control

Abstract

fetched live from OpenAlex

Public safety professionals, first responders, and active or retired members of the Canadian Armed Forces navigate high-stress work environments where they can be exposed to abhorrent materials and traumatic events. The consequences can include the development of mental health disorders, including posttraumatic stress disorder and substance use disorders. The current study, based on semi-structured interviews with 30 public safety professionals who received inpatient treatment at a recovery center in Ontario, Canada, qualitatively analyzes the roles and relationships (e.g. willingness to be open) between peers during recovery. The authors discuss ways forward for the clinical mental health treatment of public safety professionals.

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)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.437

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0210.007
Scholarly communication0.0050.002
Open science0.0030.005
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.279
Teacher spread0.233 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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