Reliability assessment of a coding scheme for the physical risk factors of work-related musculoskeletal disorders
Bibliographic record
Abstract
OBJECTIVES: This study assessed the reliability of a novel coding scheme for physical risk factors for musculoskeletal disorders reported to an occupational surveillance scheme. METHODS: Since 1997 new cases of musculoskeletal disease have been reported as part of a surveillance scheme by over 300 consultant rheumatologists in the United Kingdom; the rheumatologists also gave a short description of the tasks and activities they considered to be causal. With the use of a summary of the activities described, a coding scheme was developed comprising 16 categories of task codes and another 16 categories of movement codes. Four reviewers coded the work activities independently for 576 cases. The fourth rater coded the cases twice. With the use of a single summary kappa statistic and the matrix of kappa coefficients, both interrater reliability and intrarater reliability were assessed. RESULTS: The overall interrater agreement on the task codes was good (kappa = 0.73), with the best agreement for keyboard work (kappa = 0.96) and the worst for assembly work (kappa = 0.40, kappa = 0.37). The interrater agreement on movement codes was also good (kappa = 0.79), with the best agreement for kneeling (kappa = 0.94) and the worst for materials handling (kappa = 0.10). The intrarater agreement was somewhat better than the interrater agreement with both codes. CONCLUSIONS: The results suggest that the coding scheme was, on the whole, reliable for classifying the physical risk factors reported as causal.
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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 | 0.150 | 0.258 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".