Prévention des troubles musculo-squelettiques chez les infirmiers d’un hôpital de province au Vietnam
Bibliographic record
Abstract
OBJECTIVE: To study the situation of MSDs among nursing staff and the barriers to implementing an MSD preventive intervention in Vietnamese hospitals. METHODS: A mixed design has been devised. The quantitative component aimed to study the prevalence of MSDs, the associations between MSDs and potential risk factors and consequences of MSDs; the qualitative component focused on the study of facilitators/barriers to the implementation of a MSDs prevention program in Vietnamese hospitals. RESULTS: The prevalence of lower back, neck and shoulders MSDs, over the past 12 months, was the highest in the neck (59%) and then in lower back (49%), shoulders (40%). Factors associated with these disorders are mainly the presence of stress, being a woman and work intensity. MSD-related pain has an impact on the ability to work and the quality of life. The lack of knowledge on MSDs by health care administrators inside and outside the hospitals and the lack of human resources with expertise in MSD management are important barriers to the implementation of an MSD prevention program in Vietnamese hospitals. CONCLUSIONS: MSDs represent a serious occupational health problem in hospitals. Reducing the prevalence of MSDs requires not only an increased awareness about this serious problem among administrators, but also the development of expertise in MSD management.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".