Trends in participation rates in case–control studies of occupational risk factors 1991–2017
Bibliographic record
Abstract
OBJECTIVE: Declining participation has been observed in previous epidemiological studies, could occupational risk factor epidemiology be particularly vulnerable to this trend? The objective of this study was to assess trends of participation rates in occupational case-control studies. METHODS: Five prominent occupational and epidemiological journals were pre-selected and all articles published between 1991 and 2017 were screened for case-control studies of occupational risk factors for chronic disease outcomes. The primary independent variable was median year of data collection, while the primary outcome variable was reported participation rate. We conducted linear regression, adjusting for study characteristics that included study gender mix, location of recruitment, disease outcome, and data collection method. RESULTS: A total of 180 studies published in the five journals were included in the final analysis. The mean participation was higher for cases (78.9%) than for controls (71.5%). In linear regression, a significant trend of decreasing participation was observed for both cases with a percent change of -0.50 per year (95% CI -0.75 to -0.25) for cases and a percent change of -0.95 per year (95% CI -1.23 to -0.67) for controls. After adjustment for study gender mix, location, disease outcome, and data collection method, the trend remained statistically significant for both case and control groups. CONCLUSION: Declining participation rates in case-control studies of occupational risk factors may reflect an overall decline of participation in population-based samples. Lower participation rates introduce the potential for bias and may deter future population-based studies of occupational risk factors.
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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 | 0.034 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".