Systematic evaluation of observational methods assessing biomechanical exposures at work
Bibliographic record
Abstract
OBJECTIVES: This systematic review aimed to identify published observational methods assessing biomechanical exposures in occupational settings and evaluate them with reference to the needs of different users. METHODS: We searched scientific databases and the internet for material from 1965 to September 2008. Methods were included if they were primarily based on the systematic observation of work, the observation target was the human body, and the method was clearly described in the literature. A systematic evaluation procedure was developed to assess concurrent and predictive validity, repeatability, and aspects related to utility. At least two evaluators independently carried out this evaluation. RESULTS: We identified 30 eligible observational methods. Of these, 19 had been compared with some other method(s), varying from expert evaluation to data obtained from video recordings or through the use of technical instruments. Generally, the observations showed moderate to good agreement with the corresponding assessments made from video recordings; agreement was the best for large-scale body postures and work actions. Postures of wrist and hand as well as trunk rotation seemed to be more difficult to observe correctly. Intra- and inter-observer repeatability were reported for 7 and 17 methods, respectively, and were judged mostly to be good or moderate. CONCLUSIONS: With training, observers can reach consistent results on clearly visible body postures and work activities. Many observational tools exist, but none evaluated in this study appeared to be generally superior. When selecting a method, users should define their needs and assess how results will influence decision-making.
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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 | 0.132 | 0.403 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 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".