Naturopaths in Ontario, Canada: geographic patterns in intermediately-sized metropolitan areas and integration implications
Bibliographic record
Abstract
Evaluating conventional medicine (CM) and complementary and alternative medicine (CAM) with respect to integration opportunities (such as patient referrals and professional knowledge sharing) and possible geographic implications is novel. This research utilizes nearest neighbour and local spatial autocorrelation statistical analyses and surveys directed towards Doctors of Naturopathic Medicine (NDs) and their patients to better understand the geographic patterns of NDs and potential integration qualities. While the statistical tests reveal that the offices of NDs and Doctors of Medicine (MDs) display clustered patterns in intermediately-sized census metropolitan areas in Ontario and that the majority of NDs are near MDs, proximity is not manifesting in discernible integration tendencies between NDs and MDs. The NDs polled were strongly in favour of greater integration with the CM sector (as were their patients) to: achieve better patient health outcomes and to gain efficiencies within the health care system. Yet, both surveys also indicate that the barriers to integration are substantial and, generally speaking, centre on the perception that many MDs lack respect for, and/or knowledge about, naturopathic approaches. It is speculated that as students in conventional medical schools are increasingly exposed to CAM approaches, perhaps more MDs in the future will be receptive to greater integration with CAM. Should this occur, then it is also possible that geographic proximity may be a catalyst for deeper CAM-CM integration; as it has been for CAM-CAM relationships.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".