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Record W965468777 · doi:10.1155/2014/458105

Follow-up of Participants in the Canadian Association of Gastroenterology Scholars’ Program, 2006 to 2012

2014· article· en· W965468777 on OpenAlexaffabout
Mindy Lam, Michael SL Sey, Jamie Gregor, Clarence Wong

Bibliographic record

VenueCanadian Journal of Gastroenterology and Hepatology · 2014
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsLondon Health Sciences CentreWestern UniversityCanadian Apheresis GroupUniversity of Alberta
Fundersnot available
KeywordsSubspecialtyInternal medicineGastroenterologyMedicineFamily medicine

Abstract

fetched live from OpenAlex

The Canadian Association of Gastroenterology (CAG) Scholars' Program (previously known as the Bright Lights Course) is designed to encourage trainees to consider a subspecialty career in gastroenterology. A formal analysis of the Scholars' Program performed in 2007 revealed that 82% of participants invited to the program pursued or were planning to pursue a career in gastroenterology. The positive results are consistent with the CAG's strategic plan of developing "the next generation of gastroenterology clinical practitioners, researchers, educators, and leaders" and to "attract, train, and retain the best and the brightest to gastroenterology". The present study was a follow-up analysis of participants in the Scholars' Program between 2006 and 2012. Although 93.1% of participants had an interest in gastroenterology before attending the Scholars' Program, the majority (68.7%) reported a greater interest in gastroenterology after the program. Similar to the study from 2007, the present study again illustrates the importance and success of the Scholars' Program in generating interest and retaining candidates in gastroenterology.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.998
Threshold uncertainty score0.797

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.294
Teacher spread0.275 · 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.

Study designObservational
DomainEvaluation
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
Published2014
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Gastroenterology and HepatologySame topicInnovations in Medical EducationFrench-language works237,207