Self‐reported use of natural health products among rheumatology patients: A cross‐sectional survey
Bibliographic record
Abstract
OBJECTIVES: To describe the self-reported use of natural health products (NHPs) and identify characteristics that predict selected NHP use in rheumatology patients. METHODS: We conducted a cross-sectional survey of consecutive rheumatology patients in two major clinics in Edmonton, Alberta. Survey items included demographic data, rheumatologic diagnoses, prescribed medications, NHPs, and information regarding patients' use of NHPs. Selected NHPs of interest - defined to include joint-specific products, oils with putative joint benefits, and other non-vitamin, non-mineral products - were classified by 2 reviewers. The characteristics of selected NHP users and non-users were compared using chi-squared and ANOVA tests, followed by multivariable-adjusted logistic regression. RESULTS: 1063 patients completed the survey (response rate = 36%, mean age 53 [sd 15], 70% female). 36% of respondents reported using one or more of a wide range of selected NHPs (mean 1.8, range 1-9). The most common source of NHP recommendations for selected NHP users were physicians (42%). Significant predictors of selected NHP use were: being female (aOR 1.41, 95%CI [1.05-1.90], p = 0.02), having a post-secondary degree (aOR 1.60 [1.15-2.22], p = 0.005), and the number of non-rheumatic medications (aOR 1.08 [ 1.00-1.15], p = 0.03) and NSAIDs (aOR 1.32 [1.06, 1.63], p = 0.01). Similar findings were observed among only inflammatory arthritis patients. CONCLUSIONS: Our study confirms the frequent use of selected NHPs, possibly to mitigate persistent symptoms of rheumatologic illness. Rheumatologists appear to be trusted sources of advice and recommendations on NHP use and should provide balanced counselling for their patients.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".