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Record W4396956125 · doi:10.1080/10439463.2024.2353619

The impact of COVID-19 on police officer wellness

2024· article· en· W4396956125 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePolicing & Society · 2024
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsOfficerCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PandemicBusinessPolitical scienceVirologyMedicineLawOutbreak

Abstract

fetched live from OpenAlex

Disaster policing requires considerably more effort than working under normal conditions, thereby exacerbating existing threats to employee wellness. Research suggests that such working conditions may be harmful to physical and mental health outcomes, including increasing absenteeism. This study relies on personnel records from 3,398 police officers across 12 police services to determine the extent to which the COVID-19 pandemic impacted police officer work attendance and absenteeism, controlling for officer – and community-level characteristics. Results indicate that compared to the previous time-period, work attendance decreased during the COVID-19 pandemic and absenteeism increased. Data shows that a greater proportion of officers worked fewer days during the pandemic compared to the time-period before, and a smaller proportion worked a greater number of days. Multilevel mixed effects models indicate that COVID-19 largely contributed to decreasing attendance and increasing absenteeism beyond the effects of community conditions and officer demographics.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.041
GPT teacher head0.459
Teacher spread0.418 · 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