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Record W4237218944 · doi:10.1177/120347540100500301

One-Year Followup on the Impact of a Sun Awareness Curriculum on Medical Students' Knowledge, Attitudes, and Behavior

2001· article· en· W4237218944 on OpenAlexaffabout
Kimberly Liu, Benjamin Barankin, John M. Howard, Lyn Guenther

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2001
Typearticle
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsWestern University
Fundersnot available
KeywordsCurriculumMedicineMedical educationMedical schoolSun exposureMedical knowledgeFamily medicinePedagogyPsychologyDermatology

Abstract

fetched live from OpenAlex

Background: A one-week sun awareness curriculum was developed at the University of Western Ontario to educate first-year medical students on skin cancer risks and prevention. Objective: To assess the retention of knowledge, attitudes, and behavioral practices one year after receiving education in sun awareness. Method: Three surveys were administered: before, immediately after the sun awareness teaching, and one year later. Actual practiced behavior in the past year was compared with the intended behavior. Results: Half as many sunburns were reported in the year following the sun awareness curriculum compared with the previous year. Medical students demonstrated a good retention of the knowledge learned a year earlier. However, many students still believed that a tanned appearance looks healthy. While there was intent to adopt more healthy behavior after the curriculum, the actual behavior practiced varied. Conclusions: An undergraduate medical curriculum on sun awareness can be effective in improving the knowledge, attitudes, and behaviors of future physicians.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.038
GPT teacher head0.367
Teacher spread0.330 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations17
Published2001
Admission routes2
Has abstractyes

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