Treatment Seeking and Experience with Complementary/Alternative Medicine: A Continuum of Choice
Bibliographic record
Abstract
OBJECTIVES: To examine the treatment seeking patterns of conventional medicine (CM) and complementary and alternative medicine (CAM) clients across different health problems, and to investigate whether CAM clients with different levels of experience with CAM use different modes of treatment for their health issues. DESIGN: Three-group cross-sectional, self-administered survey study. SETTINGS AND SUBJECTS: One hundred and ninety-nine (199) self-selected participants sampled from CM and CAM clinics and offices OUTCOME MEASURES: Self-reported physical health problems, treatment seeking for health problems, and type of treatment used for each health problem (CM, CAM, or both) Results: Treatment rates for non-life-threatening health issues were significantly associated with client group membership after controlling for differences in the number of health problems (partial r = 0.35, p < 0.0001), with the more experienced CAM clients reporting the highest treatment rates and the CM clients reporting the lowest rates. The newer CAM clients also differed significantly from the more experienced CAM clients in the modes of treatment used for their health issues (p < 0.0001), with the less experienced CAM clients relying more on CM alone (42.0%) or in conjunction with CAM (38.3%), whereas the experienced CAM clients used more CAM with CM (45.0%), or CAM alone (33.5%) than just CM alone (21.5%), for treating their health issues. CONCLUSIONS: CAM users may treat their health issues at higher rates than nonusers, independent of their greater number of health problems. This treatment rate increase may be associated with greater experience with CAM. CAM clients are not homogeneous in their choices of treatment modalities. Newer CAM users still rely heavily on CM treatments, whereas more experienced CAM users depend less on CM alone and more on CAM for treating their non-life-threatening health issues.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".