Musculoskeletal health in the workplace
Bibliographic record
Abstract
Musculoskeletal (MSK) problems remain the most frequent reason why individuals are absent from work, including those with workrelated musculoskeletal disorders (WRMSDs or MSDs) and those with chronic MSK problems. This paper aims to examine changes in work and the workforce since 2000; how work impacts on chronic MSK conditions and how we can help people with these conditions to stay at work. While our knowledge of the causes of WRMSDs has increased since 2000, there has been limited workplace action in reducing exposure to hazards. A life course approach is needed as individuals of all ages are reporting MSK problems. How people work has also changed and informalisation of work contracts has increased with a perceived concurrent reduction in occupational safety and health (OSH) protection. Retaining people at work with MSK problems requires compliance with relevant safety, health and diversity legislation and a risk management approach. Good and open communication within the workplace and identification of other sources of support is also necessary. Considerations must be made at the individual level (internal motivation), organisational level (a supportive manager) and self-management of symptoms. Simple case examples are provided in the paper of what works in practice as well as a proposed research agenda. Increased awareness at all levels of society of MSK health is essential.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".