Prevalence of Work-Related Musculoskeletal Disorders among Dental Workers in Enugu Metropolis, Nigeria
Bibliographic record
Abstract
A bstract Background: Work-related musculoskeletal disorders (WRMSD) are the main occupational health hazard among several clinicians, but its prevalence among dental workers in Nigeria has not been well-studied. Objective: This study evaluated the pattern and prevalence of WRMSDs among dental workers in the Enugu metropolis, Nigeria. Materials and Methods: Six hospitals with dental clinics participated in this cross-sectional survey in the Enugu metropolis. One-hundred and fifty (150) standardized musculoskeletal symptom (Nordic) questionnaires were adopted and distributed, of which 141 were returned. The questionnaire elicited data on demographic characteristics and carrier profiles, ergonomics, and the body parts involved in the occupational activities. Results: The results indicated that 83% of the respondents sustained musculoskeletal injury more than once. Bending (66%) and performing repetitive tasks (58.2%) were the most performed risk activities. The lower back (66%) was the most affected body part, followed by the upper back (58.9%), neck (51%), shoulder (47.5%), and hip (46.1%). The most common preventive measures taken by individuals were resting (57%) and avoiding lifting (53.2%). Conclusion: There is a high prevalence of WRMSD among dental workers, with potential to having negative effect on their work habits, and reduced productivity.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".