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Record W116980077

Core competencies in surgery: evaluating the goals of urology residency training in Canada.

2006· article· en· W116980077 on OpenAlexaffabout
Kevin B Morrison, Andrew E. MacNeily

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineAccreditationSpecialtyCore competencyGraduate medical educationMedical educationHealth careTraining (meteorology)Family medicine
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: In Canada and the United States, the relevance and utility of training objectives as perceived by practising surgeons is rarely examined. We sought to determine if urology residency training objectives reflect the broad realities of urologic practice. METHODS: A survey, based on the Royal College of Physicians and Surgeons of Canada training objectives for urology, was designed and validated. All 418 full-time practising members of the Canadian Urological Association were surveyed. RESULTS: The overall response rate was 63%. Many specialized clinical areas of urology that receive little emphasis in the training objectives were rated as useful by the majority: laparoscopic surgery (92%), percutaneous renal access (86%), transrectal ultrasonography (84%), pediatric urology (81%), extracorporeal shockwave lithotripsy (70%), urethral reconstruction (66%) and adrenal surgery (62%). Microsurgery and transplantation were perceived as less useful (54% and 22% respectively). Virtually all nonsurgical training objectives were regarded as useful components of training; however, in the opinion of the majority of respondents residency did not prepare them for many of these: the challenges of office and hospital administration (91% and 89% not prepared [NP]), building a referral base (67% NP), time management (60% NP) and providing care under financial constraints (60% NP). CONCLUSION: The study results support the current training objectives and indicate areas requiring increased emphasis.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.180
GPT teacher head0.315
Teacher spread0.135 · 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

Citations19
Published2006
Admission routes2
Has abstractyes

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