The Spatial Pattern of Complementary and Alternative Medical Offices Across Ontario and Within Intermediate-Sized Metropolitan Areas
Bibliographic record
Abstract
Complementary and alternative medical approaches such as chiropractic, massage, acupuncture, holistic, and naturopathic therapies act as complements to, and in some cases replacements for, conventional medical techniques. The growing acceptance of the benefits of "traditional" medicine in the Canadian province of Ontario continues to provide complementary and alternative medicine (CAM) practitioners with business opportunities, but to date little attention has been directed toward the spatial patterns exhibited by these operations. A province-wide database of 4,599 records, containing addresses and selected characteristics (e.g., sales, employment) of CAM offices, is utilized to describe the geographic pattern across Ontario. An additional database allows for the assessment of four intermediate-sized census metropolitan areas (CMAs) in Ontario (Kingston, Guelph, Thunder Bay, and Greater Sudbury), and it is determined (using a general nearest neighbor analysis and a nearest neighbor hierarchical clustering procedure) that CAM offices are significantly clustered in specific portions of each CMA. The results from a survey administered to CAM practitioners suggests that the benefits of urbanization economies are biasing location decisions within these CMAs, and that localization economies advantages appear to be influencing complementary and alternative healthcare specialists to share offices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| 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".