Investigating food retail workers' experiences during the COVID-19 pandemic: A case of effort-reward imbalance
Bibliographic record
Abstract
Food retail businesses experienced a pronounced increase in sales when food hospitality outlets closed in the early stages of the COVID-19 pandemic in Canada. This study investigates how pandemic-related modifications to food retail businesses in Ontario, Canada affected the well-being of workers. Semi-structured interviews were conducted with 17 food retail employees between June 2020 and May 2021 as part of the Food Retail Environment Study for Health and Economic Resiliency (FRESHER). Transcripts were analyzed inductively, and themes were refined using the Effort Reward Imbalance Model. Themes were connected to the main components of this model: extrinsic effort, intrinsic effort, money, esteem, status control, and burnout. Results indicate that, for food retail employees, the presence of an imbalance between efforts and rewards threatens well-being via symptoms of burnout. Further study is needed to examine how this inequality and burnout among this population might be measured and addressed.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".