American Medical Students' Beliefs in the Effectiveness ofAlternative Medicine
Bibliographic record
Abstract
Introduction: While the use of complementary and alternative medical therapy (CAM) is common in the U.S., there have been no prior national studies of CAMrelated attitudes of U.S. medical students. Methods: We surveyed the Class of 2003 at freshman orientation, entrance to wards, and senior year in a nationally representative sample of 16 U.S. medical schools. Our primary outcome of interest was students’ Likert-scaled responses to the statement “Alternative medicine can often be as effective as traditional medicine.” Results: With 4764 responses overall (a response rate of 80.3%), 9% strongly agreed, 45% agreed, 34% neither agreed nor disagreed, 11% disagreed, and 2% strongly disagreed that alternative medicine could be as effective as traditional medicine. Students became modestly more polarized in their beliefs, moving from 37% of students neither agreeing nor disagreeing with the statement at freshman year to 31% at senior year. Several variables including gender, paternal educational level, ethnicity, religion, political self-characterization, intended specialty, and preventionorientation were associated with agreement. Conclusions: U.S. patients commonly use CAM, but newly-minted U.S. physicians’ are often skeptical about its efficacy. This disconnect may make it difficult to integrate patients’ CAM use into clinical decision-making.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".