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Record W4403229342 · doi:10.5489/cuaj.8882

Sexuality of urologist in training: Does cobbler always wear the worst shoes?

2024· article· en· W4403229342 on OpenAlexvenueno aff
Gautier Stempfer, Emilien Seizeilles De Mazancourt, Philippe Mollard, Juliette Cotte, G. Margue, Arthur Peyrottes, William Berchiche, Alice Pitout, Pierre- Luc Dequirez, Benoît Mesnard, Guillaume Joussen, Vassily Anastay, S. Lévy, Claire Deleuze, A. Goujon, Denis Séguier, Fayek Taha, Georges Mjaess, K. Kaulanjan

Bibliographic record

VenueCanadian Urological Association Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicSexual function and dysfunction studies
Canadian institutionsnot available
Fundersnot available
KeywordsTraining (meteorology)Human sexualityMedicinePsychologyMedical educationSociologyGeographyGender studies

Abstract

fetched live from OpenAlex

INTRODUCTION: Sexuality is an integral part of well-being. Urologists are a population faced with a significant workload and stress that can affect their sexuality. The purpose of this study was to investigate sexuality in this population and assess factors that may impact it. METHODS: A cross-sectional study was conducted between May and July 2023 among French urology residents and fellows. Data was collected through an anonymous questionnaire sent by the French Association of Trainee Urologists (AFUF) via email. RESULTS: Out of 445 members of the AFUF, 196 trainee urologists responded. Among them, 130 (66%) respondents were satisfied with their sexual life, and 123 (63%) reported having one or more sexual encounters per week. In univariate analysis, factors significantly impacting the level of sexual satisfaction were gender (p=0.029), level of job satisfaction/well-being (p<0.01), level of professional burnout (p<0.001), and the existence of a romantic relationship. CONCLUSIONS: Young urologists are mostly satisfied with their sexual life. Gender, level of job satisfaction/well-being, and level of professional burnout are significant factors impacting the level of sexual satisfaction.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.001
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 head0.044
GPT teacher head0.283
Teacher spread0.239 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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