Use of complementary and alternative medicine by those with a chronic disease and the general population - results of a national population based survey
Bibliographic record
Abstract
BACKGROUND: The use of complementary and alternative medicine (CAM) is becoming more common, but population-based descriptions of its patterns of use are lacking. This study aimed to determine the prevalence of CAM use in the general population and for those with asthma, diabetes, epilepsy and migraine. METHODS: Data from cycles 1.1, 2.1 and 3.1 of the Canadian Community Health Survey (CCHS) were used for the study. The CCHS is a national cross-sectional survey administered to 400,055 Canadians aged ≥12 between 2001-2005. Self-reported information about professionally diagnosed health conditions was elicited. CCHS surveys use a multistage stratified cluster design to randomly select a representative sample of Canadian household residents. Descriptive data on the utilization of CAM services was calculated and logistic regression was used to determine what sociodemographic factors predict CAM use. RESULTS: Weighted estimates show that 12.4% (95% Confidence Interval (CI): 12.2-12.5) of Canadians visited a CAM practitioner in the year they were surveyed; this rate was significantly higher for those with asthma 15.1% (95% CI: 14.5-15.7) and migraine 19.0% (95% CI: 18.4-19.6), and significantly lower for those with diabetes 8.0% (95% CI: 7.4-8.6) while the rate in those with epilepsy (10.3%, 95% CI: 8.4-12.2) was not significantly different from the general population. CONCLUSION: A large proportion of Canadians use CAM services. Physicians should be aware that their patients may be accessing other services and should be prepared to ask and answer questions about the risks and benefits of CAM services in conjunction with standard medical care.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".