Complementary and alternative medicine: A survey of its use in children with chronic respiratory illness.
Bibliographic record
Abstract
BACKGROUND: The use of complementary and alternative medicine (CAM) has increased in recent years, with especially high prevalence in individuals with chronic illnesses. In the United States, the prevalence of CAM use in pediatric asthma patients is as high as 89%. OBJECTIVE: To investigate the epidemiology of pediatric CAM use in respiratory subspecialty clinics. METHODS: A survey was conducted at two hospital-based respiratory clinics in Edmonton (Alberta) and Ottawa (Ontario). Caregivers (most often parents) of children <18 years of age were asked questions regarding child and caregiver use of CAM, including products and practices used, beliefs about CAM, trust in information sources about CAM and characteristics of the respondents themselves. RESULTS: A total of 202 survey questionnaires were completed (151 from Edmonton and 51 from Ottawa). Pediatric CAM use in Edmonton was 68% compared with 45% in Ottawa, and was associated with caregiver CAM use, poorer health and health insurance coverage for CAM. The majority (67%) of children using CAM had taken prescription drugs concurrently and 58% of caregivers had discussed this with their doctor. DISCUSSION: Lifetime use of CAM at these pediatric clinics was higher than reported for children who do not have chronic diseases. CAM practices that are popular may be worthy of further research to evaluate their effectiveness and safety profile with regard to drug interactions. Health care providers should be encouraged to discuss CAM use at every visit, and explore their patient's health-related beliefs, behaviours and treatment preferences.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".