MétaCan
Menu
Back to cohort
Record W1502934 · doi:10.5070/d39k26h0d1

A survey of dermatology residency program directors' views on mentorship

2009· article· en· W1502934 on OpenAlexaff
Jeff Donovan

Bibliographic record

VenueDermatology Online Journal · 2009
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMentorshipMedicineProgram directorCareer developmentFamily medicineMedical education

Abstract

fetched live from OpenAlex

BACKGROUND: It is increasingly recognized that mentoring is important in the career development of resident physicians. The purpose of the study was to determine the views of residency Program Directors on mentorship through a cross sectional survey. METHODS: Respondents included program directors of academic dermatology departments in the United States. RESULTS: Fifty-three of 108 program directors completed an on-line survey (response rate 49%). Eighty-one percent of respondents indicated that mentorship played a 'somewhat' or 'very important' role in their own career development and a similar proportion considered it important for residents to have mentors. Fifty percent of program directors identified a need for more structured mentorship within the residency program. Compared to male program directors, a greater proportion of female program directors stated that mentorship played a very important role in their career development (89% vs. 36%, p=0.007) and a lesser proportion stated that it was important for female dermatology residents to specifically have access to female mentors (11.1% vs. 67.4%, p=0.003). CONCLUSION: Program Directors viewed mentoring as an important resource for their residents' professional development. A need was identified for additional strategies to help residents find mentors.

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)0.004
metaresearch head score (Gemma)0.010
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 score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.085
GPT teacher head0.403
Teacher spread0.318 · 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

Citations25
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueDermatology Online JournalSame topicMentoring and Academic DevelopmentFrench-language works237,207