The Prevalence and Associated Risk Factors of Shoulder Injuries in Primary School Teachers, Durban, South Africa
Bibliographic record
Abstract
BACKGROUND: Shoulder injuries are among the most common musculoskeletal disorders (MSD) that can present in teachers, due to the nature of the teaching profession. OBJECTIVE: To determine the prevalence and associated risk factors of shoulder MSD in primary school teachers, Durban, South Africa. METHODS: A cross-sectional study was conducted on 203 school teachers. A questionnaire to determine the prevalence of shoulder injuries and other common injuries experienced was completed. Descriptive statistics and chi-square and binomial tests were used to analyse the results. RESULTS: The prevalence of shoulder injuries among school teachers was 53.7%, which was significantly higher than neck injuries (p=.037). Participants who had had a previous injury to the shoulder were more likely to have experienced shoulder problems at work (p = .006). A significant 76.1% had not injured their shoulder in any way (p <.0005). Additionally, the shoulder problems prevented a significant 77% of the participants from performing their normal work for up to seven days during the previous 12 months (p<.0005). CONCLUSION: Preventative and management strategies for shoulder injuries among school teachers are needed.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".