Recruitment for Occupational Research: Using Injured Workers as the Point of Entry into Workplaces
Bibliographic record
Abstract
OBJECTIVE: To investigate the feasibility, costs and sample representativeness of a recruitment method that used workers with back injuries as the point of entry into diverse working environments. METHODS: Workers' compensation claims were used to randomly sample workers from five heavy industries and to recruit their employers for ergonomic assessments of the injured worker and up to 2 co-workers. RESULTS: The final study sample included 54 workers from the workers' compensation registry and 72 co-workers. This sample of 126 workers was based on an initial random sample of 822 workers with a compensation claim, or a ratio of 1 recruited worker to approximately 7 sampled workers. The average recruitment cost was CND$262/injured worker and CND$240/participating worksite including co-workers. The sample was representative of the heavy industry workforce, and was successful in recruiting the self-employed (8.2%), workers from small employers (<20 workers, 38.7%), and workers from diverse working environments (49 worksites, 29 worksite types, and 51 occupations). CONCLUSIONS: The recruitment rate was low but the cost per participant reasonable and the sample representative of workers in small worksites. Small worksites represent a significant portion of the workforce but are typically underrepresented in occupational research despite having distinct working conditions, exposures and health risks worthy of investigation.
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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 | 0.101 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".