Comparison of expert-rater methods for assessing psychosocial job strain
Bibliographic record
Abstract
OBJECTIVES: This study tested the reliability and validity of industry- and mill-level expert methods for measuring psychosocial work conditions in British Columbia sawmills using the demand-control model. METHODS: In the industry-level method 4 sawmill job evaluators estimated psychosocial work conditions at a generic sawmill. In the mill-level method panels of experienced sawmill workers estimated psychosocial work conditions at 3 sawmills. Scores for psychosocial work conditions were developed using both expert methods and applied to job titles in a sawmill worker database containing self-reported health status and heart disease. The interrater reliability and the concurrent and predictive validity of the expert rater methods were assessed. RESULTS: The interrater reliability and concurrent reliability were higher for the mill-level method than for the industry-level method. For all the psychosocial variables the reliability for the mill-level method was greater than 0.90. The predictive validity results were inconclusive. CONCLUSIONS: The greater reliability and concurrent validity of the mill-level method indicates that panels of experienced workers should be considered as potential experts in future studies measuring psychosocial work conditions.
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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 | 0.112 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".