Towards global health equity: The contributions of health geography to global health research, policy and practice
Bibliographic record
Abstract
As part of the tripartite evolution, health geography emerged alongside the population health perspective and a shift from the biomedical notion of health to an increased recognition of the role of socio-ecological factors that shape health and well-being. Owing to these shifts, health geographers have made substantial contributions to global health research in the areas of theoretically informed research, methodological innovation and diversity and evidence-based research translating into health policy and practice. By establishing the reciprocal relationship between place and human health, health geographers have expanded global health scholarship by demonstrating that geography and health are inextricably linked. This review provides a background of recent developments in health geography and demonstrates how health geographers can leverage their expertise by adopting new technologies in the face of emerging global health challenges.
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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 | 0.044 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.071 |
| Scholarly communication | 0.019 | 0.035 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".