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Record W2147051499 · doi:10.1080/01421590701827353

Genetics education in medical school: a qualitative study exploring educational experiences and needs

2008· article· en· W2147051499 on OpenAlexaffabout
Deanna Telner, June Carroll, Yves Talbot

Bibliographic record

VenueMedical Teacher · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedical geneticsPsychosocialHuman geneticsMedical educationMedicineQualitative researchGenetic counselingFamily medicinePsychologyGeneticsPsychiatryBiologySociology

Abstract

fetched live from OpenAlex

BACKGROUND: Genetic discoveries increasingly have an impact on clinical medicine. Primary care providers (PCPs) need to be prepared to address patients' concerns about their genetic risks. AIMS: To explore family medicine residents' experiences with genetics in medical school and residency training and to understand their educational needs in genetics. METHODS: Four focus groups were held with 33 family medicine residents at the University of Toronto, which represented graduates of 9 different Canadian medical schools. Groups were audio-taped, transcribed and analysed independently by 4 reviewers using content analysis. Recurrent themes were identified. RESULTS: Participants described their experiences with genetics in medical school as almost entirely related to rare disorders, so genetics was not perceived to be clinically relevant. There was little awareness of the complex ethical and psychosocial issues that accompany genetics. However, participants felt that genetics would become significant in medical care in the future and PCPs would play an important role. They expressed a need for more knowledge of genetics to fulfill this role and practical teaching in genetics by clinicians. CONCLUSIONS: Medical school educational experiences may not be preparing future PCPs to address genetic issues with patients. A change and a broadening of the teaching of genetics are required to fulfill this need.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.055
GPT teacher head0.393
Teacher spread0.337 · 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 teacher head, not a consensus.

Study designQualitative
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

Citations40
Published2008
Admission routes2
Has abstractyes

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