Occupational pressures of frontline workers enforcing COVID-19 pandemic measures in Ontario and Quebec, Canada
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".