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Record W4391174972 · doi:10.56367/oag-041-10750

Canadian workers at risk: Removing barriers to treatment for public safety professionals (PSP)

2024· article· en· W4391174972 on OpenAlexaffabout
Gregory S. Andérson, Helen Dragatsi

Bibliographic record

VenueOpen Access Government · 2024
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsGovernment of CanadaThompson Rivers University
Fundersnot available
KeywordsBusinessWorkplace safetyEnvironmental healthMedicineOccupational safety and healthPathology

Abstract

fetched live from OpenAlex

Canadian workers at risk: Removing barriers to treatment for public safety professionals (PSP) Gregory S Anderson, from Thompson Rivers University and Helen Dragatsi, from Government of Canada speak to us about removing barriers to treatment for Canadian workers at risk. It has long been recognized that public safety personnel (PSP) experience a high risk of psychological impacts caused by traumatic events within their occupations. Such exposures have been linked to increased incidence of posttraumatic stress injuries (PTSI), including anxiety, posttraumatic stress disorder (PTSD), depressive disorders, suicide ideation, and substance abuse. In response, most Canadian jurisdictions (provinces and territories) have adopted new workers’ compensation legislative amendments that create a presumption in favor of PSP who suffer from trauma-induced mental disorders. In these situations, the mental disorder is presumed to have been caused by the responder’s employment. These presumptive clauses facilitate access to workers’ compensation for public safety professionals (PSP) who previously had the onus of proving that their psychological impairments resulted from their work.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.600
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.158
GPT teacher head0.531
Teacher spread0.374 · 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; both teacher heads agree on what is shown here.

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
Published2024
Admission routes2
Has abstractyes

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