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Record W2019741238 · doi:10.2310/7750.2011.10021

Teaching Dermatology to Canadian Undergraduate Medical Students

2011· article· en· W2019741238 on OpenAlexafffundabout
Carly Kirshen, Ilya Shoimer, Judy Wismer, J. P. DesGroseilliers, Harvey Lui

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2011
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsMcMaster UniversityUniversity of OttawaUniversity of British Columbia
FundersUniversité Laval
KeywordsMedicineCurriculumDermatologyMedical educationConsistency (knowledge bases)Medical schoolFamily medicinePsychology

Abstract

fetched live from OpenAlex

BACKGROUND: The Canadian dermatology undergraduate curriculum was reviewed in 1983, 1987, and 1996. All surveys revealed the limited amount of time dedicated to dermatology in the undergraduate curriculum. OBJECTIVE: This survey was designed to obtain current information regarding undergraduate dermatology teaching in Canadian medical schools. METHODS: A survey was sent electronically to all undergraduate dermatology curriculum coordinators at each of the 17 Canadian medical schools. RESULTS: Between 1996 and 2008, the average number of hours of dermatology teaching has increased by 7 hours to 20.5 ± 17.2 hours. Again, most of the teaching is performed in the preclinical years. The majority of schools would like to have more time dedicated to dermatology teaching; however, many schools cited a restriction in the number of dermatology faculty members, with an average of 7.8 ± 7 dermatologists, as a barrier to education delivery. CONCLUSION: It is important to have dermatology included throughout the undergraduate medical curriculum because most dermatologic problems are seen by nondermatologists. Respondents at each school believed that there may be value in moving toward a national strategy for dermatology curriculum changes, and this can ensure both uniformity and consistency within Canada.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.290
Teacher spread0.259 · 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.

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

Citations28
Published2011
Admission routes3
Has abstractyes

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