Spatial‐temporal trends in complementary and alternative medicine (CAM) offices in Ontario, Canada
Bibliographic record
Abstract
This paper assesses complementary and alternative medicine (CAM) from a spatial‐temporal perspective; it would be of particular interest to those who evaluate health care resource accessibility over space. The analysis compares CAM supply (number of offices, employment, and sales) in Ontario by provincial district, metropolitan influence classification, and health care and social assistance employment quintiles using summary statistics, Kruskal‐Wallis and median analyses, and local spatial autocorrelation evaluation. Metropolitan areas throughout Ontario, but especially in the southcentral part of the province, are well endowed with CAM supply and tend to be most important in terms of CAM change. CAM offices are increasing in size in the most populated parts of the province and shrinking in regions that are more peripheral. CAM supply per capita is highest in census subdivisions with moderate levels of health care and social assistance employment, a result that is not offset by significant temporal change. While CAM supply is restructuring in many of Ontario's most populated urban locations, the overall attraction of CAM resources to large and small metropolitan areas is clear. If current spatial‐temporal trends continue, CAM spatial disparities will be exacerbated as accessibility to CAM in Ontario's most peripheral locations worsen.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".