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Record W2124952395 · doi:10.1155/2006/715042

2006 CAG Educational Needs Assessment Report

2006· article· en· W2124952395 on OpenAlexaffvenueabout
Alaa Rostom, Sandra Daniels

Bibliographic record

VenueCanadian Journal of Gastroenterology · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsCanadian Apheresis Group
Fundersnot available
KeywordsMedical educationMedicine

Abstract

fetched live from OpenAlex

T he annual survey of the Canadian Association ofGastroenterology (CAG) members' educational needs was conducted via keypad assessment during Canadian Digestive Diseases Week (CDDW) 2006, and also in a more detailed paper and on-line survey during March and April 2006.Up to 156 members completed the abbreviated keypad questions and 345 members answered the detailed survey.Similar to previous years, inflammatory bowel disease (IBD) topics were most in demand for future educational events.Other highly rated areas were pharmacological therapeutics, nutrition, autoimmune and metabolic liver disease, and celiac disease.Educational materials were judged to be the most valuable component of the exhibit areas (76%), and 32% reported repeated use of the on-line CAG Interactive Lecture Series.The purpose of the CAG needs assessment was to provide guidance to the Executive and the CAG Education Committee on areas of greatest educational need.Conducting a needs assessment is a requirement for accreditation of educational events according to the Royal College of Physicians and Surgeons of Canada accreditation criteria.

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)0.008
metaresearch head score (Gemma)0.019
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.618
Threshold uncertainty score0.769

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0050.005
Science and technology studies0.0030.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.004

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 head0.017
GPT teacher head0.324
Teacher spread0.307 · 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
Published2006
Admission routes3
Has abstractyes

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