Do US medical students report more training on evidence-based prevention topics?
Bibliographic record
Abstract
Little is known about the extent to which evidence-based prevention topics are taught in medical school. All class of 2003 medical students (n = 2316) at 16 US schools were eligible to complete three questionnaires: at the beginning of first and third years and in their senior year, with 80.3% responding. We queried these students about 21 preventive medicine topics, concerning the extent of their training and their patient counseling frequency at some of these time points. At the beginning of the third year, self-reported extensive training was low for all preventive medicine topics (range 7-26%). USPSTF-recommended topics received more curricular time (median for topics: 36% if recommended versus 24.5% if not, P = 0.025), as did topics addressed through testing rather than through discussion (median for topics: 37% for testing and 25% for discussion, P = 0.005). Extensive training was always associated with higher counseling frequency, and intention to go into primary care, female gender, a positive attitude toward prevention and positive personal health habits were associated with higher counseling frequency. Although some bemoan the overall low levels of US medical students' prevention-related training and practice, we demonstrate that at least they are preferentially evidence-based, a novel and encouraging finding for preventionists.
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".