Methodological issues in evaluating workplace interventions to reduce work-related musculoskeletal disorders through mechanical exposure reduction
Bibliographic record
Abstract
Researchers of work-related musculoskeletal disorders are increasingly asked about the evidentiary base for mechanical exposure reductions. Mixed messages can arise from the different disciplinary cultures of evidence, and these mixed messages make different sets of findings incommensurate. Interventions also operate at different levels within workplaces and result in different intensities of mechanical exposure reduction. Heterogeneity in reporting intervention processes and in measuring relevant outcomes makes the synthesis of research reports difficult. As a means of synthesizing the current understanding of measures, this paper describes a set of intervention and observation nodes for which relevant workplace indicators prior to, during, and after mechanical exposure reduction can provide useful information. On the basis of this path of impacts from exposure reduction, an approach to the evaluation of multilevel ergonomic interventions is described that can assist fellow researchers in producing evidence relevant to the challenges faced by workplace parties and policy makers.
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 | 0.681 | 0.829 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".