Supermarket workers: Their work and their health, particularly their self-reported musculoskeletal problems and compensable injuries
Bibliographic record
Abstract
A literature review revealed that cashiers are the most studied of all supermarket workers, while little is known about other types of employees. However, cashiers are far from being the only supermarket workers affected by musculoskeletal disorders. The musculoskeletal health of supermarket employees other than cashiers was therefore examined for one company. Two sources of data were used: compensation statistics (from the company's 57 corporate supermarkets) and self-reported questionnaires (administered in 4 selected stores). These sources provided very different descriptive statistics, both in terms of the size of problems (depending on which aspects were compared, compensation statistics depicted 2 to 18 times fewer disorders than self-reports), and in terms of which body regions were most affected. There were also discrepancies with regard to identifying those departments which were most at risk (wrappers according to self-reports, delicatessen according to compensation reports). According to self-reports, 83% of workers (excluding cashiers) reported at least one musculoskeletal disorder over a 12-month period, and 32% had problems severe enough to impede regular activities. Different approaches to calculating rates were also used within each data source. Calculations using the number of hours worked annually by all workers were deemed to be the best. The significance of these results for supermarket employees and in terms of intervention and prevention in other sectors is examined.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".