There is an urgent need for a global rural health research agenda
Bibliographic record
Abstract
People living in rural areas generally experience adverse health outcomes compared to their urban counterparts. They experience a greater burden of non-communicable diseases including: diabetes, hypertension, stroke, kidney disease, and chronic obstructive pulmonary disease (COPD), have limited access to healthcare services, and experience scarcity in specialized healthcare services. The disproportionately high all-cause mortality experienced by rural residents has been termed "the rural mortality penalty". With over 90% of the world's rural population living in Africa and Asia, we argue that the lack of an authoritative and respected global rural health research agenda contributes to increasing health inequalities, given that many of these people are receiving substandard care. There are differences in how rural and urban resident's experience healthcare. Living in rural settings might not be systematically connected to adverse health outcomes. It is important to clearly articulate the positive health outcomes associated with living in rural settings (e.g., the positive relationship between mental health and strong social ties/green spaces). Indeed, health policies stand the chance of unconsciously excluding the positive outcomes associated with rurality, as well as the rural experiences of health. Defining rural health remains an issue of controversy with a persistent reality regarding the lack of consensus as to what it means for a region or area to be considered as "rural". We outline the most common definitions of "rural areas" in the literature, as well as the shortcomings of these definitions. By unpacking the meaning of "rural health", we aim to foster communication among rural health professionals and researchers locally and internationally, as well as highlight the key research and policy implications that could emanate from a "good" definition of rural health. We agree that context remains key when it comes conceptualizing complex subjects like rurality. However, developing minimum criteria to foster communication among rural health researchers is needed. Systematically providing operational definitions of what authors describe as "rural" in the rural health research and policy literature is of utmost relevance.
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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 | 0.032 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".