Connecting Health Promotion and Sustainable Development Goals to Enhance the Health of Agrifood Temporary Migrant Workers—a Scoping Review
Bibliographic record
Abstract
ABSTRACT Temporary migrant workers (TMW) in the agrifood system face social determinants of health (SDoH) that contribute to health inequities. Despite their dual residency, the potential of the Sustainable Development Goals (SDG) to support the health of TMW remains underexamined. SDG transformations provide a framework for implementing interventions and policies. This scoping review aimed to map health promotion interventions and policies for agrifood TMW in high‐income countries by SDoH addressed and categorize them with SDG transformations. Twenty‐six studies were identified from multidisciplinary databases; characteristics and SDoH were charted. While key SDoH were addressed, most interventions and policies clustered within two transformations: ‘Health’ and ‘Education and inequalities’. Gaps in remaining SDG transformations highlight opportunities to strengthen agrifood social and environmental sustainability. By linking health promotion interventions with SDG transformations, this review offers a novel strategy to guide intersectoral, participatory efforts to support TMW health and equity, and inform future research and policy.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 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".