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Record W2900874735 · doi:10.5489/cuaj.5573

Exploring the business of urology: Is it time for a “Business of Healthcare” curriculum in urology residency programs?

2018· article· en· W2900874735 on OpenAlexaffvenueabout
Darren Beiko, Christopher Gonzalez, Arthur Mourtzinos, Eugene Rhee

Bibliographic record

VenueCanadian Urological Association Journal · 2018
Typearticle
Languageen
FieldHealth Professions
TopicDental Education, Practice, Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsUrologyCurriculumMedical educationMedicineHealth carePsychologyPedagogyPolitical science

Abstract

fetched live from OpenAlex

rology is a relatively small surgical specialty filled with innovative and forward-thinking physicians. Decade after decade, through early adoption of new technologies, urologists have been at the cutting edge of advances in medical and surgical care. Urologists are generally very well-prepared for the rapidly evolving improvements medical and surgical technology. But are graduating urologists prepared for the business side of their practices and careers? After completion of postgraduate training and upon entry into the workforce, are most urologists prepared for the multiple roles and demands that will be placed upon them in today's dynamic healthcare environment? Does residency provide adequate education and evalution of basic business topics and principles that are critical to achieving high performance in one's job as a urologist? Do we adequately train urologists to manage all aspects of the operations of their own practice, including the negotiation and legal execution of employment agreements and contracts? Are urologists adequately prepared to navigate the Canadian and U.S. healthcare landscapes and trends in the delivery of care? Unfortunately, the answer to these questions for most young urologists is, "No."

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)0.006
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.156
GPT teacher head0.435
Teacher spread0.279 · 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

Citations3
Published2018
Admission routes3
Has abstractyes

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