How Precarious Employment Affects Health and Safety at Work: The Case of Temporary Agency Workers
Bibliographic record
Abstract
Precarious employment has been associated with adverse occupational health and safety (OHS) outcomes across a range of studies. Temporary agency workers are particularly vulnerable, with studies showing they experience a higher incidence of workplace injury, and a greater likelihood of more severe injuries than all other employment types. Explanations for agency workers’ higher risk of injury have, to date, been impeded by data limitations associated with researching temporary employment. This article seeks to begin filling this gap through analyzing the experience of agency workers based upon two data sources. The first is a unique qualitative and quantitative data set developed from investigated temporary agency and directly hired workers’ compensation files; the second is focus groups of agency workers conducted in the State of Victoria, Australia. Quinlan and Bohle’s (2004) Pressures, Disorganization and Regulatory Failure (PDR) model, developed to explain the greater OHS vulnerability of precarious workers, provides the framework for analyzing the data. After explaining the key concepts in the PDR Model, the article analyses the data to test for evidence of economic pressures, disorganization at the workplace, and regulatory failure impacting upon temporary agency workers’ health and safety. The analysis supports the relevance of the PDR model, and provides an understanding of additional and unique risk factors which contribute to agency workers’ higher risk of injury. Temporary agency workers experience economic pressures in common with other types of precarious workers. However, these appear more acute amongst agency workers. They also confront disorganization risks, extending to mismatched placements; lack of familiarity with host workplaces; and more complex fractured communication. These contribute to workplace risks and create barriers to improving their experience. Many of these outcomes are a result of, or contribute to regulatory failure. The analysis finds strong support for the explanatory value of the PDR model as a tool for understanding how precariousness contributes to temporary agency workers’ adverse health and safety outcomes. It also suggests the complexities of the triangular employment relationship create additional economic insecurities and disorganization problems beyond those experienced by other types of workers, which the regulatory environment has yet to address.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| 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".