Physical employment standards, physical training and musculoskeletal injury in physically demanding occupations
Bibliographic record
Abstract
BACKGROUND: Physically demanding occupations such as the military, firefighting and law enforcement have adopted physical employment standards (PES). The intent of PES is to match the physical capacity of personnel with the physical demands of job tasks. Inadequate physical capacity can affect occupational task performance as well musculoskeletal injury (MSKI) risk. OBJECTIVE: To present contemporary evidence on the relationship(s) between PES, physical training, physical capacity and MSKI in physically demanding occupations, and provide recommendations regarding physical training for improved occupational performance and reduced MSKI risk. METHODS: This narrative review draws on evidence from 104 published sources. RESULTS: Physical training is central to the development and maintenance of occupationally-relevant physical capacity, as well as mitigating MSKI risk associated with job performance. In addition, given the prevalence of manual handling tasks, strength training needs to be emphasised in physical training regimen. CONCLUSIONS: PES development can inform both physical training and injury prevention strategies in physically demanding occupations. Furthermore, a physical performance continuum is essential to through-career maintenance of occupational performance and health, and the preservation of organisational capability. Finally, organisations should consider the potential to implement PES as maximal performance tests to better understand the relationship between occupational task performance and MSKI risk.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".