Prevalence of Musculoskeletal Disorders and Their Risk Factors among Professional Gardeners
Bibliographic record
Abstract
Background: Work-related musculoskeletal disorders (WMSDs) are a significant occupational health issue, particularly among professional gardeners due to the physically demanding nature of their tasks. Prolonged awkward postures, repetitive activities, and heavy manual labor contribute to the high prevalence of these disorders. Objective: To systematically review the prevalence, associated risk factors, and preventive strategies for WMSDs among professional gardeners. Methods: A systematic review was conducted in accordance with PRISMA guidelines. Searches were performed in Ovid MEDLINE, CINAHL, EMBASE, and the Cochrane Database of Systematic Reviews for studies published up to May 2022. Inclusion criteria were peer-reviewed studies focusing on professional gardeners and reporting prevalence or risk factors of WMSDs. Data extraction included study design, population characteristics, outcomes, and risk assessments. Quality appraisal was performed using the Newcastle-Ottawa Scale and JBI tools. Results: Fifteen descriptive studies met the inclusion criteria. Across studies, the most affected body regions were the lower back, knees, shoulders, and neck. Common risk factors included repetitive bending and twisting, heavy lifting, prolonged kneeling, and use of non-ergonomic tools. Prevalence rates for WMSDs were consistently high, with lower back pain being the most common complaint. Ergonomic interventions, training programs, and improved tool design showed potential in reducing injury risk. Conclusion: Professional gardeners face a high burden of WMSDs, primarily due to physically strenuous tasks and poor ergonomic practices. Targeted preventive measures, including ergonomic modifications, worker training, and health monitoring, are essential to reduce the incidence of these disorders. Keywords: Musculoskeletal Disorders, Professional Gardeners, Ergonomic Risk Factors, Occupational Health, Work-Related Injuries
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".