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Record W4402511332 · doi:10.3233/wor-230315

Occupational pressures of frontline workers enforcing COVID-19 pandemic measures in Ontario and Quebec, Canada

2024· article· en· W4402511332 on OpenAlexaffabout
Pamela Hopwood, Ellen MacEachen, Daniel Côté, Samantha B. Meyer, Shannon E. Majowicz, Ai-Thuy Hyun, Meghan Crouch, Joyceline Amoako, Yamin Tauseef Jahangir, Amelia León Correal, Antonela Ilic

Bibliographic record

VenueWork · 2024
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversité de MontréalInstitut de recherche Robert-Sauvé en santé et en sécurité du travailUniversity of Waterloo
Fundersnot available
KeywordsPandemicEnforcementCoronavirus disease 2019 (COVID-19)Occupational safety and healthPublic healthBusinessWork (physics)Situational ethicsPsychological interventionEnvironmental healthPublic relationsNursingMedicinePsychologyPolitical scienceEngineeringSocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: During the COVID-19 pandemic, low-wage public-facing frontline workers (FLWs), such as grocery store clerks, were required to monitor retail customers and enforce COVID-19 protocols. OBJECTIVE: This analysis aimed to examine FLWs experiences of enforcing COVID-19 pandemic measures. METHODS: Between September 2020 and March 2021, in Ontario and Quebec (Canada), we conducted in-depth interviews about customer-related work and health risks with FLWs who interacted with the public (n = 40) and their supervisors (n = 16). Using a lens of situational analysis, verbatim transcripts were coded according to recurring topics. RESULTS: We found that enforcing public health measures placed already-precarious workers in difficult occupational health circumstances. Enforcement of measures created additional workplace responsibilities, stress, and exposed them to potentially negative reactions from customers. CONCLUSIONS: Interventions to better support these workers and improved methods of protection are discussed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.090
GPT teacher head0.386
Teacher spread0.297 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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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