Lay versus expert understandings of workplace risk in the food service industry: A multi-dimensional model with implications for participatory ergonomics
Bibliographic record
Abstract
The recent trend towards cooperative management and prevention of workplace injuries has introduced numerous health and safety actors to the workplace with varying amounts and types of expertise. The purpose of this qualitative research project was to explore the understandings of risk as experienced by food service workers (FSW) and how these compare with an 'expert' in risk assessment. In total 13 FSW, selected based on age, work location, and gender, and one experienced Ergonomist participated in the study. In-depth semi-structured telephone interviews were conducted with each participant and transcripts of the interviews were analyzed using thematic analysis by drawing on methods closely related to grounded theory. The findings of this study indicated that the risks for occupational injury as experienced by FSW were multi-dimensional in nature representing not only the physical requirements of the individual's job, but also the social interactions of the FSW with their coworkers, management, and the organization. FSW were also found to be a rich source of knowledge and experience concerning occupational risk and may be under-utilized when designing interventions. The results of this study support a cooperative team approach to reduce the risks of injury in the workplace, with a specific emphasis on inclusion of the worker.
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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 | 0.025 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".