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Record W2982631274 · doi:10.5489/cuaj.5643

Evaluation of inter-professional communication and leadership skills among graduating Canadian urology residents.

2019· article· en· W2982631274 on OpenAlexaffabout
Gregory Hosier, Naji J. Touma

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsQueen's University
Fundersnot available
KeywordsCommunication skillsMedical educationSelf-assessmentMedicinePsychologyPedagogy

Abstract

fetched live from OpenAlex

INTRODUCTION: The importance of developing inter-professional communication and leadership skills among residents is well-recognized; however, formal tools to assess these skills are lacking. The goal of our study was to assess the leader and communicator roles in graduating urology residents using a validated self-assessment form developed for business students that focuses on inter-professional skills. METHODS: Chief residents (n=36) were evaluated with surveys of communication and leadership skills. The same surveys were administered through email to the residents' program directors (PDs). Resident self-assessment and PD assessment were compared using paired and non-paired t-tests. RESULTS: Graduating urology residents' self-assessment of their communication and leadership skills did not differ from assessments made by their PDs (77.6 vs. 74.4%; p=0.19); however, there were outlier candidates in whom PD assessment differed substantially from self-assessment on both surveys. Graduating urology residents scored themselves higher on self-awareness (82.6 vs. 77%; p= 0.05) and lower on stress management (67.7 vs. 77%; p= 0.01) compared to their PDs. Resident self-assessment scores were similar to business students on both communication and leadership surveys. Limitations were the small sample size and lack of survey evaluation by those surveyed. CONCLUSIONS: Graduating urology residents' self-assessment of their own communication and leadership skills did not differ greatly from assessment by their PDs or a sample of business students. Comparison of self-assessment evaluations and evaluations by PDs allowed us to identify outliers in whom self-assessment and PD-assessment markedly differed, which may allow for more focused and meaningful feedback.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.995
Threshold uncertainty score0.811

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.094
GPT teacher head0.337
Teacher spread0.243 · 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

Citations1
Published2019
Admission routes2
Has abstractyes

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