Mapping the Way to Good Health: The Interdisciplinary Challenges of Geographers in Medical Research
Bibliographic record
Abstract
Geography has an important role to play in shaping the direction of medical research. In particular, its tools and theory provide essential understanding to the impacts of place on health behaviors and outcomes. Understanding some of its evolution-particularly into the subfield of medical geography-is therefore useful both for geographers and medical researchers. In this paper, we present some of the debates that geographers have grappled with, the growth of GIS (particularly in the context of medical research), some important methodological considerations that geographers help center, and some recommendations for future work at this nexus. Throughout, we speak from the perspective of geographers who have worked nearly exclusively in the health sciences since obtaining our PhDs.
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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 | 0.114 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.014 | 0.077 |
| Scholarly communication | 0.026 | 0.047 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 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".