Use of Complementary and Alternative Medicine by HIV-Infected Outpatients in Ontario, Canada
Bibliographic record
Abstract
Little is known about complementary and alternative medicine (CAM) use in Canadian patients with HIV. We sought to determine the prevalence of CAM use by patients attending HIV outpatient clinics in Ontario, Canada, and to compare the characteristics of users and nonusers. Impact of CAM definition on reported utilization rates was also assessed, specifically in relation to the inclusion and exclusion of vitamins, minerals, and multivitamins in CAM definition. In-person interviews were conducted between 1999 and 2001 with 104 HIV-positive outpatients enrolled in the HIV Ontario Observational Database project (HOOD) and attending HIV outpatient clinics in Ontario. Self-reported CAM utilization and demographic data were collected. Clinical data were obtained from medical chart review. Seventy-seven percent of participants reported current CAM use. Inclusion of vitamins and minerals (CAMVIT) increased this estimate to 89%. Nearly all patients used CAM in conjunction with antiretroviral medications. Out of pocket costs ranged from CAD$0 to more than CAD$250 per month. Most patients reported CAM use was beneficial and had improved their overall health. Female gender, HIV risk group, number of prescriptions, and overall number of drugs used were associated with CAM use. CAM use in Canadian patients with HIV is extremely common, with higher use among women. The definition of CAM has a substantial impact both on reported prevalence rates and on predictors of CAM use.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".