Assessing the Role of Evidence in Patients' Evaluation of Complementary Therapies: A Quality Study
Bibliographic record
Abstract
BACKGROUND: Making the decision to use complementary and alternative medicine (CAM) for cancer treatment is difficult in light of the limited available evidence for these treatments. It is unclear how patients use evidence to make these decisions. OBJECTIVES: (1) Describe the type of information about CAM that cancer patients use in their decision making; (2) understand why certain types of information about CAM are accepted as evidence by cancer patients; and (3) explore the role of scientific evidence in treatment decision making. METHODS: A qualitative study design using in-depth semistructured interviews with cancer patients attending 4 conventional and integrative health care institutions in Alberta and British Columbia, Canada, was used. RESULTS: Twenty-seven patients were interviewed. Patients sought CAM information from a range of sources, including the Internet, health care providers, friends, relatives, and newspapers. Many expressed frustration about the overwhelming amount of available information and found it difficult to identify reliable information. Information was described as reliable if it supported them in arriving at a decision about CAM. Types of information participants identified included anecdotes, expert opinion, gut feeling, popular literature, scientific evidence, testimonials, advertising and trial and error. Profound differences were found between new CAM users, experienced CAM users, and users with late-stage cancer in type of information sought, the role of scientific evidence in decision making, and overall information needs. CONCLUSION: Although this was a relatively small qualitative study, the results suggest that (1) many patients do not value scientific evidence as highly as conventional providers and (2) it is important for clinicians and other information providers to be aware of the different types of information that patients seek out and access when making choices and decisions regarding CAM treatments and why they seek out these sources.
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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 | 0.478 | 0.731 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".