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Record W1629387358 · doi:10.5489/cuaj.2919

Perception, career choice and self-efficacy of UK medical students and junior doctors in urology

2015· article· en· W1629387358 on OpenAlexvenueno aff
Patrick Jones, Bhavan Prasad, Hasan Qazi, Bhaskar Somani, Ghulam Nabi

Bibliographic record

VenueCanadian Urological Association Journal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsUrologyMedicinePerceptionMedical educationFamily medicinePsychology

Abstract

fetched live from OpenAlex

INTRODUCTION: There is a growing concern about the reduced clinical exposure to urology at undergraduate level in the United Kingdom. As a consequence, the competencies of junior doctors are considered inadequate. The views of these doctors in training towards urology remain under reported. METHODS: A modified Delphi method was employed to construct a questionnaire. Given the rise of social media as a platform for scientific discussion, participants were recruited via a social networking site. Outcomes assessed included career preference, exposure to urology, perceived male dominance, and confidence at core procedures. RESULTS: In total, 412 and 66 responses were collected from medical students and junior doctors, respectively. Overall, 41% of participants felt that they had received a good level of clinical exposure to urology as part of their training and 15% were considering a career in this speciality. Female students were significantly less likely to consider urology as a career option (p < 0.01). Of these, 37% of the students felt confident at male catheterization and 46% of students regarded urology as a male-dominated speciality. CONCLUSIONS: Urology is perceived as male dominated and is the least likely surgical speciality to be pursued as a career option according to our survey. Increased exposure to urology at the undergraduate level and dedicated workshops for core urological procedures are needed to address these challenges.

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)0.004
metaresearch head score (Gemma)0.004
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 score0.044
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
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 head0.028
GPT teacher head0.293
Teacher spread0.265 · 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

Citations29
Published2015
Admission routes1
Has abstractyes

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