Translation of mechanical exposure in the workplace into common metrics for meta-analysis: a reliability and validity study
Bibliographic record
Abstract
OBJECTIVES: We previously assessed inter-rater reliability of expert raters using six scales to estimate the intensity of literature-based mechanical exposures. The objectives of this study were to estimate the impact on the inter-rater reliability of using non-expert (NE) raters and to assess the validity of our scales. METHODS: We used 7-point scales to represent three dimensions of mechanical exposures at work: 1) trunk posture, 2) weight lifted or force exerted and 3) spinal loading. We estimated both peak and cumulative loads and called this an "interpretive translation" of exposure. A second method, "algorithmic translation", used the original units in which the exposure data was collected. These data were used to assess the inter-rater reliability and validity of the NE interpretive translation of exposure. RESULTS: The NE inter-rater reliability for the scales ranged from 0.24 to 0.46. The correlation between the means of the NE and expert ratings were >0.7. Although there was a strong relationship between the NE interpretive and the algorithmic translation, there was some evidence that the interpretive translation plateaus at higher level of exposure. CONCLUSIONS: This study supports using NE raters to estimate the intensity of literature-based mechanical exposure metrics using a common set of scales which can be applied across epidemiologic studies. We would need to average the ratings of at least five NE raters to have an acceptable level of reliability (>0.7). These metrics may be useful to quantify the relationship between workplace mechanical exposure and low back pain in a systematic review and meta-analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.361 | 0.611 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.025 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".