Bibliographic record
Abstract
Despite worldwide attention for more than four decades, musculoskeletal disorders (MSDs) remain a substantial concern at work and result in considerable personal and societal burden.This slow progress is not for want of trying.Prevention of MSDs has been emphasized in multiple jurisdictions.For example, in 2007 the European Agency for Safety and Health at Work organized a major campaign, "Lighten the Load -How to prevent Musculoskeletal Disorders (MSDs)" and NIOSH in the USA specifically identified MSDs as a major focus in their National Occupational Research Agenda.However, the results from surveys, published sick leave, and lost time data indicate we have a way to go in preventing MSDs.Which leaves us with a question: Why have we not solved the MSD problem?The initiatives mentioned above (and others) have uncovered multiple research questions including: the effects of new forms of work, the interaction of psychosocial and mechanical exposures, changing demographics, risk assessment, identification of best practice, and the implementation of interventions in companies.While all these are clearly relevant questions, we still need to know which of the answers to theseor any other question -will drive us forward in the prevention of work-related MSDs.In order to help refine the research agenda for the Centre of Research Expertise for the Prevention of Musculoskeletal Disorders, as director, I have begun to view the prevention of MSDs around six questions.I see these six questions as a flow of logic that can be used as a heuristic.The answers to each question can help identify weak links, and prioritize where the Centre's
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 | 0.019 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.007 | 0.019 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.024 | 0.035 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".