Complementary medical health services: a cross sectional descriptive analysis of a Canadian naturopathic teaching clinic
Bibliographic record
Abstract
BACKGROUND: Historically, alongside regulatory and jurisdictional differences in scope of practices, practice patterns of naturopathic doctors (NDs) have varied widely to promote holistic or whole-person treatment using a variety of therapies including: controlled substances, minor surgery, a variety of complementary therapies, as well as both novel and conventional assessments. However, little is known about the observed practice patterns of NDs, the services provided to their patients, or the type of conditions for which patients of NDs are seeking treatment. In order to address this gap, a cross-sectional descriptive analysis of the largest Canadian teaching clinic for NDs was undertaken to better understand the services provided to the community and increase the knowledge regarding the use of naturopathic medicine. METHODS: Data stemmed from two sources at the Toronto, Ontario clinic: a passive patient satisfaction survey, and the clinic's point-of-sale (POS) system. Data included patient demographics, postal codes, health services utilization, ICD-10 codes, therapies employed, along with other data relating to the financial transactions associated with the visit. Simple descriptive statistics and the Kruskal-Wallis test were used to compare different age-based groups and examine health services use between years. This study was approved by the Research Ethics Board of the Canadian College of Naturopathic Medicine. RESULTS: 13,412 patients were treated in 76,386 patient visits spanning three clinic years. Median age of patients was 37; females outnumbered males (2.6:1) in all age-based groups except the pediatric population. In the patient satisfaction survey, there were 1552 potential survey respondents; with 118 responses received (response rate: 7.6%). Obtaining health education, health prevention and help with chronic health conditions were the primary motivators for patient visits identified in the patient survey. CONCLUSION: The clinic attracts people from a wide area in the metropolitan Toronto and surrounding region with health concerns and diagnoses that are consistent with primary care, providing health education and addressing acute and chronic health conditions. Further explorations into health services delivery from the broader naturopathic or other complementary/alternative medical professions would provide greater context to these findings and expand understanding of the patients and type of care being provided by these health professionals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".