Workers’ experience with work-related musculoskeletal disorder and worker’s perception of organisational policies and practices
Bibliographic record
Abstract
Purpose Different organisations have developed policies and programmes to prevent workplace injuries and facilitate return to work. Few multiple workplace studies have examined workers’ perceptions of these policies and programmes. The purpose of this paper is to compare workers’ perception and experience of workplace policies and practices on injury prevention, people-oriented work culture, and return to work. Design/methodology/approach This study recruited 118 workers from three healthcare facilities through an online and paper survey. Findings Work-related musculoskeletal injury was experienced by 46 per cent of the workers, with low back injuries being most prevalent. There were significant differences in perception of policies and practices for injury prevention among occupational groups, and between workers who have had previous workplace injury experience and those without past injury. Research limitations/implications Selection bias is possible because of voluntary participation. A larger sample could give stronger statistical power. Practical implications The perception of workplace policies can vary depending on workers’ occupational and injury status. Organisational managers need to pay attention to the diversity among workers when designing and implementing injury prevention and return to work policies. Social implications Risks for workplace injuries are related to multiple factors, including workplace policies and practices on health and safety. Workers’ understanding and response to the policies, programmes, and practices can determine injury outcomes. Originality/value No previous study has reported on workers’ perceptions of workplace policies and practices for injury prevention and return in Manitoba healthcare sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".