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Record W3215106530 · doi:10.1177/26338076211058250

Fear of infectious diseases and perceived contagion risk count as an occupational health and safety hazard: Accounts from correctional officer recruits in Canada

2021· article· en· W3215106530 on OpenAlexaffabout
Marcella Siqueira Cassiano, Fatih Ozturk, Rosemary Ricciardelli

Bibliographic record

VenueJournal of Criminology · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsOfficerHazardDistancingOccupational safety and healthPerspective (graphical)MedicinePsychologyCoronavirus disease 2019 (COVID-19)Political scienceInfectious disease (medical specialty)Law

Abstract

fetched live from OpenAlex

Prisons are poorly ventilated confined spaces with limited physical distancing opportunities, making an environment conducive to the spread of infectious diseases. Based on empirical research with correctional officer recruits in Canada, we analyze the reasons and sources of fear, and the measures that recruits adopt to counter their fear of contagion. Our study marks an advance in the correctional work literature, which, to date, has tended to view perceived contagion risks as a workplace challenge that can be overcome with occupational skill and experience. In contrast with the existing literature, we present fear and perceived contagion risk as an “operational stress injury” that affects all correctional officers; a structural occupational health and safety problem that needs redressing from the labor policy perspective.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.272

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0150.005
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.003
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 head0.063
GPT teacher head0.389
Teacher spread0.327 · 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

Citations15
Published2021
Admission routes2
Has abstractyes

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