Equity in Whom Gets Studied: A Systematic Review Examining Geographical Region, Gender, Commodity, and Employment Context in Research of Low Back Disorders in Farmers
Bibliographic record
Abstract
Farmers are at high risk of having low back disorders (LBDs). Agriculture employs half the global workforce, but it is unclear whether all farming populations are represented equitably in the LBD literature. This systematic review quantifies the number and quality of research studies by geographical region, agricultural commodity, and farmer characteristics. MEDLINE, Web of Science, CINAHL, Scopus, and Embase databases were searched using conceptual groups of search terms: "farming" and "LBD." Screening and extraction were performed by two researchers in parallel, then reconciled through discussion. Extracted study characteristics included location of study; commodity produced; worker sex, ethnicity, and migration status; type of employment; and study quality. These were compared with agricultural employment statistics from the International Labour Organization and World Bank. From 125 articles, roughly half (67) did not specify the employment context of the participants in terms of migration status or subsistence versus commercial farming. Although in many regions worldwide women make up the bulk of the workforce, only a minority of low back disorder studies focus on women. Despite the predominance of the agricultural workforce in developing nations, 91% of included studies were conducted in developed nations. There was no significant difference in study quality by geographic region. The nature of the world's agricultural workforce is poorly represented by the literature when it comes to LBD research. If developing nations, female sex, and migrant work are related to increased vulnerability, then these groups need more representation to achieve equitable occupational health study.
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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 | 0.025 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.016 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".