Why patients with inflammatory bowel disease use or do not use complementary and alternative medicine: A Canadian national survey
Bibliographic record
Abstract
BACKGROUND: The use of complementary and alternative medicine (CAM) is common in patients with inflammatory bowel disease (IBD). OBJECTIVES: To determine the factors associated with use of CAM, the reasons commonly cited for use or nonuse of CAM, and the correlations between the factors associated with use of CAM and reasons for CAM use. SUBJECTS: The study included 2828 members of the Crohn's and Colitis Foundation of Canada. METHODS: Subjects were mailed a questionnaire that included items on demographic characteristics, disease and treatment history, health attitudes and behaviours, and reasons for use or nonuse of CAM. Logistical regression was used to determine significant associations with current CAM use. RESULTS: In patients with Crohn's disease and ulcerative colitis, CAM use was associated with more severe disease activity, use of CAM for other purposes, use of exercise and prayer for IBD, and a desire for an active role in treatment decisions. CAM use was also associated with younger age in those with Crohn's disease, and less confidence in their IBD physician in those with ulcerative colitis. The most common reasons for CAM use were a desire for greater control, having heard or read that CAM might help, and the emphasis CAM places on treating the whole person. The most common reasons for not using CAM were that conventional treatments were successful, that not enough was known about CAM and a belief that CAM would not help. CONCLUSION: Disease activity and health attitudes and behaviours, but not demographic characteristics, are associated with CAM use by those with IBD.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".