The development of a conceptual model and self-reported measure of occupational health and safety vulnerability
Bibliographic record
Abstract
Injuries at work have a substantial economic and societal burden. Often groups of labour market participants, such as young workers, recent immigrants or temporary workers are labelled as being "vulnerable" to work injury. However, defining groups in this way does little to enable a better understanding of the broader factors that place workers at increased risk of injury. In this paper we describe the development of a new measure of occupational health and safety (OH&S) vulnerability. The purpose of this measure was to allow the identification of workers at increased risk of injury, and to enable the monitoring and surveillance of OH&S vulnerability in the labour market. The development included a systematic literature search, and conducting focus groups with a variety of stakeholder groups, to generate a pool of potential items, followed by a series of steps to reduce these items to a more manageable pool. The final measure is 29-item instrument that captures information on four related, but distinct dimensions, thought to be associated with increased risk of injury. These dimensions are: hazard exposure; occupational health and safety policies and procedures; OH&S awareness; and empowerment to participate in injury prevention. In a large sample of employees in Ontario and British Columbia the final measure displayed minimal missing responses, reasonably good distributions across response categories, and strong factorial validity. This new measure of OH&S vulnerability can identify workers who are at risk of injury and provide information on the dimensions of work that may increase this risk. This measurement could be undertaken at one point in time to compare vulnerability across groups, or be undertaken at multiple time points to examine changes in dimensions of OH&S vulnerability, for example, in response to a primary prevention intervention.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".