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Record W2166038991 · doi:10.1093/her/cys073

Do US medical students report more training on evidence-based prevention topics?

2012· article· en· W2166038991 on OpenAlexaff
Erica Frank, Sheira Schlair, Lisa Elon, Mona Saraiya

Bibliographic record

VenueHealth Education Research · 2012
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineFamily medicinePrimary careMedical educationMedical schoolPreventive healthcareHealth carePsychologyNursingPublic health

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.043
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0430.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.696
GPT teacher head0.739
Teacher spread0.044 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2012
Admission routes1
Has abstractyes

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