Intention to Encourage Complementary and Alternative Medicine Among General Practitioners and Medical Students
Bibliographic record
Abstract
The authors' goal was to identify factors explaining intention to encourage a patient to follow complementary and alternative medicine (CAM) treatment among general practitioners (GPs), fourth-year medical students, and residents in family medicine. They surveyed 500 GPs and 904 medical students via a self-administered mailed questionnaire that they based on the Theory of Planned Behavior. Respondents expressed a neutral level of intention to encourage CAM approach. Variables explaining 75% of variance of intention of all participants were: moral norm, beta=0.34, p<.0001; perceived behavioral control, beta=0.29, p<.0001; attitude, beta=0.22, p<.0001; descriptive norm, beta=0.13, p<.0001; and professional status, (GPs, beta=-0.07, p<.0001; residents, beta=-0.07, p<.0001). Facilitating conditions and developing a better perception of control over perceived obstacles could help enhance health-care practitioners' intentions to use CAM. Also, a clear position on the part of the medical community would help to define a professional norm in line with the moral norm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".