The association between awkward working posture and low back disorders in farmers: a systematic review
Bibliographic record
Abstract
BACKGROUND: Low back disorders (LBD) are the most common musculoskeletal problem among farmers, with higher prevalence than other occupations. Although studies of the general population have shown an association between LBD and awkward working posture, farmers have unique work context and exposures that may modify this relationship. This review aimed to 1) identify published research studies investigating posture as a risk factor for LBD in farmers/agricultural workers, and 2) determine the strength of the relationship between postural exposure and LBD risk of bias assessment. METHOD: Comprehensive electronic searches of Medline, Web of Science, CINAHL, SCOPUS, PubMed, and EMBASE were carried out with combined conceptual groups of search terms for 'farming' and 'LBD.' After screening, data were extracted to summarize the study design, sample characteristics, exposure assessment methods, LBD risk factors, demographic information, data collection methods, farm commodities, job context, and sampling strategy. Data were synthesized to determine the weight of evidence for awkward working posture as a risk factor for LBD among farmers. RESULTS: Nine studies were included in this review. All studies used self-report; there were no field-based studies including direct measurement of awkward posture. There was diversity in exposure definition, exposure assessment, LBD definition, worker characteristics, and analytical approaches. There was evidence to support association between awkward working posture and LBD among farmers. CONCLUSION: Despite the diversity, the weight of evidence supported a relationship between awkward posture and LBD. Well-designed epidemiological studies with quantitative physical workload assessments, consistent and valid LBD definitions, and longitudinal designs are recommended to clarify the relationship between awkward posture and LBD.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".