A geographic assessment of ‘total’ health care supply in Ontario: complementary and alternative medicine and conventional medicine
Bibliographic record
Abstract
Shortages of family physicians, specialists and other personnel working within the realm of conventional medicine (CM) concern citizens in many regions and municipalities in Canada. Complementary and alternative medicine (CAM) approaches (such as chiropractic, holistic, homeopathic, naturopathic, massage and acupuncture) are increasingly used in conjunction with, or in some cases as replacements for, conventional medicine. Thus, to get an idea of ‘total’ health care supply in a jurisdiction and to draw comparisons between locations, it is useful to understand the spatial tendencies of both CM and CAM offices. With the use of a sample that contains the location, employment and sales of 4,955 CAM and 8,709 CM offices, this study details the spatial patterns of health care supply in the Canadian province of Ontario. The analysis comprises three main parts. First, the geographic tendencies of CAM and CM office activity are revealed in per capita terms and while regional differences are detectable, the main contrast is that CAM displays a much more even distribution across the urban‐rural continuum in comparison to CM. Second, through the use of location quotients and a local spatial autocorrelation analysis, it is shown that certain municipalities (especially in Ontario's southwest and south‐central regions) specialize in CAM and the most outstanding spatial feature is an ‘81 municipality CAM cluster’ that represents arguably the pinnacle of CAM activity in the province. CM specialization is rarer and is biased towards the more populated municipalities. Third, a Spearman's correlation analysis suggests that CAM and CM health care supply are associated with community well‐being indictors and urban density measures.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".