Complementary and Alternative Medicine Use in Canada and the United States
Bibliographic record
Abstract
Use of complementary and alternative medicine (CAM) has stimulated discussion in both Canada1–4 and the United States5–12 on topics such as who might benefit from CAM insurance coverage and the role of CAM as a substitute for use of conventional medical treatment vs a supplement to such treatment. In the United States, members of racial or ethnic minority groups are less likely to use CAM than are White people, and elevated income is a strong predictor of CAM use.5,6,8 In the United States (unlike in Canada), race and ethnicity are related closely to health insurance status.13 In both Canada4 and the United States,5,6,8 CAM use appears higher in western regions than in other areas. In Canada, western provinces are much more likely than those in the east to cover CAM in their health programs.1 In the United States, some 42 states mandate coverage of chiropractic care in private insurance,9 whereas federal legislation mandates coverage for all people older than 65 years (in the Medicare program) as well as for individuals whose health insurance is provided by large employers regulated under the Employee Retirement Income Security Act.14 This study examined relationships between race, geography, and conventional medical care and the use of acupuncture, chiropractic, homeopathy/naturopathy, and massage therapy.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".