The Impact of Health Geography on Public Health Research, Policy, and Practice in Canada
Bibliographic record
Abstract
The link between geography and health means that the places we occupy-where we are born, where we live, where we work, and where we play-have a direct impact on our health, including our experiences of health. A subdiscipline of human geography, health geography studies the relationships between our environments and the impact of factors that operate within those environments on human health. Researchers have focused on the social and physical environments, including spatial location, patterns, causes of disease and related outcomes, and health service delivery. The work of health geographers has adopted various theories and philosophies (i.e., positivism, social interactionism, structuralism) and methods to collect and analyze data (i.e., quantitative, qualitative, spatial analysis) to examine our environments and their relationship to health. The field of public health is an organized effort to promote the health of its population and prevent disease, injury, and premature death. Public health agencies and practitioners develop programs, services, and policies to promote healthy environments to support and enable health. This commentary provides an overview of the recent landscape of health geography and makes a case for how health geography is critically important to the field of public health, including examples from the field to highlight these links in practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.029 | 0.019 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".