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Record W2745106478 · doi:10.1177/1203475417725876

Undergraduate Dermatology Education in Canada: A National Survey

2017· article· en· W2745106478 on OpenAlexaffabout
Angela Hu, Ronald Vender

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2017
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsDermatrials ResearchMcMaster University
Fundersnot available
KeywordsCurriculumMedicineDermatologyMedical educationFamily medicinePsychologyPedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: Canadian dermatology curriculum was reviewed in 1983, 1987, 1996, and 2008. All these surveys highlighted the disproportionately low level of dermatology teaching in relation to the significant amount of skin disease seen by physicians. Since the official adoption and dissemination of the Canadian Professors of Dermatology (CPD) core curriculum and competencies, there has been no assessment of how these changes have influenced dermatology curriculum. OBJECTIVE: This survey gathered information on the current status of undergraduate dermatology education across Canadian medical schools. METHODS: A survey was sent electronically to all undergraduate dermatology directors at each of the 17 Canadian medical schools. RESULTS: Between 2008 and 2017, dermatology teaching has increased 25% to 25.6 ± 17.2 hours of teaching. However, 75% of this teaching is delivered in preclinical years. The number of faculty members, both dermatologists and nondermatologists, has also increased. A growing number of schools are now using electronic formats of teaching. Most schools (59%) are covering all the CPD core curriculum topics. CONCLUSION: Dermatology education is demonstrating positive trends with regards to teaching hours and faculty members. Nevertheless, a more even distribution of content so that students have increased clinical exposure should be achieved. Furthermore, an online atlas of resources would be helpful in standardising curriculum.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score0.835

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)0.0000.000

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 head0.039
GPT teacher head0.302
Teacher spread0.263 · 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 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

Citations32
Published2017
Admission routes2
Has abstractyes

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