Work intensification and health outcomes of health sector workers
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the mediating role of stress between work intensification and musculoskeletal disorders (MSDs) focusing on personal support workers (PSWs) in home and community care. Design/methodology/approach The analysis sample of 922 comes from the 2015 survey of PSWs employed in Ontario, Canada. The endogenous variable is self-reported MSDs, and the exogenous variable is work intensification. Stress, measured as symptoms of stress, is the mediating variable. Other factors shown in the literature as associated with stress and/or MSDs are included as control variables. Structural equation model regression analyses are presented. Findings The results show that stress mediates the effect of work intensification on PSW’s MSDs. Other significant factors included being injured in the past year, facing hazards at work and preferring less hours – all had positive and significant substantive effects on MSDs. Research limitations/implications The survey is cross-sectional and not longitudinal or experimental in design, and it focuses on a single occupation in a single sector in Ontario, Canada and, as such, this can limit the generalizability of the results to other occupations and sectors. Practical implications For PSW employers including their human resource managers, supervisors, schedulers and policy-makers, the study recommends reducing work intensification to lower stress levels and MSDs. Originality/value The findings of this study contribute to the theory and knowledge by providing evidence on how work intensification can affect workers’ health and assist decision makers in taking actions to create healthy work environments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".