Attitudes Toward Integration of Complementary and Alternative Medicine with Hospital-Based Care
Bibliographic record
Abstract
OBJECTIVE: To characterize those who have used, expect to use, or are opposed to the use of holistic therapies, especially in a conventional medical (hospital) setting. SAMPLE DESCRIPTION AND METHODS: Cross-sectional survey of a random sample of Hamilton-Wentworth residents between March and June 1998 (n = 416; response rate, 63%); analysis used logistic regression. RESULTS: Thirty-seven percent (37%) used at least one holistic therapy in the previous year: the three most common were chiropractic, massage, and herbal/phytology. The three most common reasons for use were general health, fatigue, and arthritis. Thirty-three percent (33%) would use holistic therapy in the future. Barriers to use were lack of information, perceived ineffectiveness, and cost; approximately 40% agreed they would only use holistic therapies with medical advice. Approximately 13% were opposed to holistic therapy and objected to its use in hospitals. Younger age, preference for holistic therapy over conventional medicine, and prior use of holism independently predicted high likelihood for future use. Lower income and high self-perceived health were associated with negative attitude toward use of holistic therapies in hospital. CONCLUSION: Most respondents would accept integration of holistic techniques into a hospital; therapies would be more acceptable if there were clear evidence of their efficacy. A few might find their opinion of a sponsoring hospital lowered by such integration.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".