Should studies of risk factors for musculoskeletal disorders be stratified by gender? Lessons from the 1998 Québec Health and Social Survey
Bibliographic record
Abstract
OBJECTIVES: Several studies have reported male-female differences in the prevalence of symptoms of work-related musculoskeletal disorders (MSD), some arising from workplace exposure differences. The objective of this paper was to compare two strategies analyzing a single dataset for the relationships between risk factors and MSD in a population-based sample with a wide range of exposures. METHODS: The 1998 Québec Health and Social Survey surveyed 11 735 respondents in paid work and reported "significant" musculoskeletal pain in 11 body regions during the previous 12 months and a range of personal, physical, and psychosocial risk factors. Five studies concerning risk factors for four musculoskeletal outcomes were carried out on these data. Each included analyses with multiple logistic regression (MLR) performed separately for women, men, and the total study population. The results from these gender-stratified and unstratified analyses were compared. RESULTS: In the unstratified MLR models, gender was significantly associated with musculoskeletal pain in the neck and lower extremities, but not with low-back pain. The gender-stratified MLR models identified significant associations between each specific musculoskeletal outcome and a variety of personal characteristics and physical and psychosocial workplace exposures for each gender. Most of the associations, if present for one gender, were also found in the total population. But several risk factors present for only one gender could be detected only in a stratified analysis, whereas the unstratified analysis added little information. CONCLUSIONS: Stratifying analyses by gender is necessary if a full range of associations between exposures and MSD is to be detected and understood.
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 | 0.120 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 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; 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".