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MP10-20 INPATIENT WORKLOAD HAS INCREASED BY UP TO 300% OVER THE LAST DECADE: ANALYSIS OF A UROLOGY RESIDENCY TRAINING PROGRAM FROM 2007-2017

2019· article· en· W2940654227 on OpenAlexaboutno aff
Adam Kinnaird, Logan Zemp, Andrew Rasmussen, Michael Chetner, Keith Rourke, Trevor Schuler

Bibliographic record

VenueThe Journal of Urology · 2019
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineWorkloadHealth careUrologyManagement

Abstract

fetched live from OpenAlex

also mastered a greater percentage of questions (24% vs. 16%, p< 0.001).Almost half of residents in the team-based competition, 45%, answered every question versus only one third of those in the individual-incentive arm, 33% (p[0.01).CONCLUSIONS: The inclusion of behavioral incentives in medical education enhanced resident engagement.Specifically, learner participation and knowledge mastery in a QI curriculum increased with team-based competition.Additionally, we demonstrated the feasibility of national implementation of a specialty-specific QI curriculum.Further studies are needed to investigate the effects of behavioral interventions in other medical education contexts.

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)0.010
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 score0.569
Threshold uncertainty score0.867

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.009
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.053
GPT teacher head0.406
Teacher spread0.352 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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