Development of an International Questionnaire to Measure Use of Complementary and Alternative Medicine (I-CAM-Q)
Bibliographic record
Abstract
OBJECTIVES: Existing studies on the use of complementary and alternative medicine (CAM) have produced diverse results regarding the types and prevalence of CAM use due, in part, to variations in the measurement of CAM modalities. A questionnaire that can be adapted for use in a variety of populations will improve CAM utilization measurement. The purposes of this article are to (1) articulate the need for such a common questionnaire; (2) describe the process of questionnaire development; (3) present a model questionnaire with core questions; and (4) suggest standard techniques for adapting the questionnaire to different languages and populations. METHODS: An international workshop sponsored by the National Research Center in Complementary and Alternative Medicine (NAFKAM) of the University of Tromsø, Norway, brought CAM researchers and practitioners together to design an international CAM questionnaire (I-CAM-Q). Existing questionnaires were critiqued, and working groups drafted content for a new questionnaire. A smaller working group completed, tested, and revised this self-administered questionnaire. RESULTS: The questionnaire that was developed contains four sections concerned with visits to health care providers, complementary treatments received from physicians, use of herbal medicine and dietary supplements, and self-help practices. A priori-specified practitioners, therapies, supplements, and practices are included, as well as places for researcher-specified and respondent-specified additions. Core questions are designed to elicit frequency of use, purpose (treatment of acute or chronic conditions, and health maintenance), and satisfaction. A penultimate version underwent pretesting with "think-aloud" techniques to identify problems related to meaning and format. The final questionnaire is presented, with suggestions for testing and translating. CONCLUSIONS: Once validated in English and non-English speaking populations, the I-CAM-Q will provide an opportunity for researchers to gather comparable data in studies conducted in different populations. Such data will increase knowledge about the epidemiology of CAM use and provide the foundation for evidence-based comparisons at an international level.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".