Complementary and Alternative Medicine Use in Canada and the United States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it