Use of complementary and alternative therapies by patients self-reporting arthritis or rheumatism: results from a nationwide canadian survey.
Bibliographic record
Abstract
OBJECTIVE: Arthritis or rheumatism (A/R) often leads patients to experiment with complementary and alternative medicine (CAM); we investigated the factors associated with use of CAM. METHODS: The source of the data is the cross sectional household component of the 1996-97 National Population Health Survey of the health status and behaviors of Canadians. The survey sample is based on 66,000 persons aged 20 years and older, representing 21 million adults. Cross tabulations were used to estimate the percentage of adults with A/R who used CAM. Multivariate logistic regression was used to identify those characteristics associated with the use of CAM in the year preceding the survey. RESULTS: In 1996-97, among the 3.3 million Canadian adults aged 20 years or older who self-reported arthritis, 22% utilized CAM in the past year. CAM users tended to be younger and with higher education and household income. They reported more pain, consumed more analgesics, and tended to be more depressed. The coexistence of back or bowel disorders, cancer, sinusitis, or food allergies with arthritis was also related to CAM use. Moreover, CAM users also used more traditional health resources. CONCLUSION: Our results indicate that patients with A/R consulting CAM providers self-report more intense symptoms than nonusers and often have other chronic conditions. They do not seem to reject the traditional health care system, but supplement it with CAM, possibly to fulfill needs insufficiently satisfied by traditional health care providers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".