Prevalence of musculoskeletal discomfort, occupational working factors, and work demands amongst food service kitchen workers in Ontario Canada
Bibliographic record
Abstract
With high rates of reported physical pain/discomfort as well as co-occurrences of pain localization, workers in the food service industry are at a higher risk for musculoskeletal disorder development than many other working populations. The purpose of this paper was to document the prevalence as well as workplace conditions and demands of food service workers in Ontario Canada. A cross-sectional survey was completed from September 2022 to October 2023. The survey was completed in an online format using Qualtrics software and the sample population consisted of current and past food service workers in Ontario. Responses were counted and presented as percentages of the total respondents. 108 food service kitchen workers completed the survey. The prevalence of musculoskeletal discomfort at any anatomical location was 98.1% with 88.9% reporting discomfort at multiple locations. The prevalence of discomfort at specific anatomical locations was highest for the lower back (68.5%), followed by the feet (65.7%), shoulders (47.2%), neck (43.5%), and mid to upper back (42.6%). Most workers reported not typically taking periodic breaks during their work shift (75.0%), spending most of their time standing at their workstations (77.8%), having not received ergonomic training (50.0%), not exercising regularly (63.0%), and having a medium to high level of task variety for which they are responsible (89.8%) during a shift. The prevalence of musculoskeletal discomfort and co-occurrences are high among food service workers in Ontario. The overall working conditions appear to include many risk factors that may help explain this high prevalence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".