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Record W2005644227 · doi:10.2310/7750.2012.12016

Teaching Dermatology to Internal Medicine Residents: Needs Assessment Survey and Possible Directions

2013· article· en· W2005644227 on OpenAlexaffabout
Aaron M. Drucker, Rodrigo B. Cavalcanti, Brian M. Wong, Scott R. Walsh

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2013
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsToronto Western HospitalUniversity of TorontoHealth Sciences CentreUniversity Health NetworkSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineDermatologyRespondentAmbulatorySpecialtyInternal medicineEmergency medicineFamily medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Internal medicine trainees receive limited teaching and training in dermatology and may feel inadequately prepared to assess and manage patients with dermatologic complaints. No study to date has assessed the needs of internal medicine trainees in Canada with regard to dermatology teaching. OBJECTIVE: To determine internal medicine residents' comfort in assessing and managing dermatologic issues and their educational needs in dermatology. METHODS: An electronic survey was conducted of first-, second-, and third-year internal medicine residents at the University of Toronto. RESULTS: Fifty-four of 186 internal medicine trainees responded to our survey (response rate = 29%). Each respondent did not answer every question. Residents were generally uncomfortable or very uncomfortable assessing and managing dermatologic issues in the emergency department (40 of 47, 85%), ward or intensive care unit (39 of 47, 83%), and ambulatory clinic (40 of 47, 85%). Residents thought that various clinical and didactic dermatology exposures would be useful to their training as internists. Case-based teaching and ambulatory clinical rotations were felt to be particularly valuable. Additionally, 38 of 46 (83%) respondents wanted to learn how to perform punch biopsies. CONCLUSIONS: An effort should be made to increase the availability of relevant dermatology teaching and clinical exposures for internal medicine residents.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.615

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 integrity0.0000.001
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 head0.025
GPT teacher head0.308
Teacher spread0.283 · 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

Citations27
Published2013
Admission routes2
Has abstractyes

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