Risk factors for work‐related musculoskeletal disorders: a systematic review of recent longitudinal studies
Bibliographic record
Abstract
OBJECTIVE: This systematic review was designed and conducted in an effort to evaluate the evidence currently available for the many suggested risk factors for work-related musculoskeletal disorders. METHODS: To identify pertinent literature we searched four electronic databases (Cinahl, Embase, Medline, and The Cochrane Library). The search strategies combined terms for musculoskeletal disorders, work, and risk factors. Only case-control or cohort studies were included. RESULTS: A total of 1,761 non-duplicated articles were identified and screened, and 63 studies were reviewed and integrated in this article. The risk factors identified for the development of work-related musculoskeletal disorders were divided and organized according to the affected body part, type of risk factor (biomechanical, psychosocial, or individual) and level of evidence (strong, reasonable, or insufficient evidence). CONCLUSIONS: Risk factors with at least reasonable evidence of a causal relationship for the development of work-related musculoskeletal disorders include: heavy physical work, smoking, high body mass index, high psychosocial work demands, and the presence of co-morbidities. The most commonly reported biomechanical risk factors with at least reasonable evidence for causing WMSD include excessive repetition, awkward postures, and heavy lifting. Additional high methodological quality studies are needed to further understand and provide stronger evidence of the causal relationship between risk factors and work-related musculoskeletal disorders. The information provided in this article may be useful to healthcare providers, researchers, and ergonomists interested on risk identification and design of interventions to reduce the rates of work-related musculoskeletal disorders.
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 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".