Prevalence of musculoskeletal disorders and postural analysis of beekeepers
Bibliographic record
Abstract
Work-related musculoskeletal disorders (WRMSDs) lead to fatigue and decreased productivity in workers, resulting in the need for many affected individuals to seek medical treatment annually. Beekeepers , like other agricultural workers, are susceptible to WRMSDs due to the continuous demands of their work and the repetitive movements involved. Thus, the objective of this study is to determine the prevalence of WRMSDs and assess the level of risk associated with different postures among beekeepers to improve their musculoskeletal health. To achieve this, a cross-sectional study was conducted involving 33 beekeepers, consisting of two stages. Firstly, the Nordic Questionnaire was utilized to assess the prevalence of WRMSDs. Subsequently, the Ovako Working Posture Analysis System (OWAS) was employed to analyze and categorize the riskiest postures into four levels of corrective measures. The findings indicate that the most commonly affected areas were the back (51.5%) and waist (45.4%). The occurrence of WRMSDs in various body regions was significantly associated with the beekeepers’ years of experience and weekly working hours. Additionally, the prevalence of neck and back pain was significantly related to their body mass index (BMI). The OWAS postural analysis revealed that the back (36.75%) and arm (21.08%) regions required corrective measures as soon as possible (level III), while the back (26.47%) and legs (14.70%) fell under the category of corrective measures needed in the near future (level II). Combining the postural analysis results, 28.43% were classified as Action Levels (AL) II, 37.73% as level III, and 0.98% as level IV. This study demonstrates that WRMSDs are relatively common among beekeepers, primarily due to their extensive work experience and the adoption of awkward postures during their tasks. As a result, recommendations regarding ergonomics and physiotherapy are provided to alleviate pain and reduce the strain on critical postures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".