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Record W1687284477 · doi:10.25011/cim.v30i4.2831

70. Supporting IMG integration into residency trainings

2007· article· en· W1687284477 on OpenAlexvenueaboutno aff
Susan Glover Takahashi, Mitchell Alameddine, Dawn Martin, Sarita Verma, Sarah Edwards

Bibliographic record

VenueClinical and investigative medicine · 2007
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMedical educationCurriculumResidency trainingProfessional developmentWorkforceSession (web analytics)Faculty developmentIMGMedicineWorkforce developmentPsychologyPedagogyContinuing educationPolitical scienceComputer science

Abstract

fetched live from OpenAlex

This paper is describes the design, development, implementation and evaluation of a preparatory training program for international medical trainees. The program was offered for one week full time shortly before they begin their residency training programs. First the paper reports on the survey and focus groups that guided the learning objectives and the course content. Next the paper describes the curriculum development phase and reports on the topical themes, session goals and objectives and learning materials. Three main themes emerged when developing the program: understanding the educational, health and practice systems in Canada; development of communication skills; and supporting personal success in residency training including self assessment, reflection and personal wellness. Sample lesson plans and handouts from each of the theme areas are illustrated. The comprehensive evaluation of the sessions and the overall program is then also described. The paper then summarizes the identified key issues and challenges in the design and implementation of a preparatory training program for international medical trainees before they begin their residency training programs. Allan GM, Manca D, Szafran O, Korownyk C. Workforce issues in general surgery. Am Surg. 2007 Feb; 73(2):100-8. Dauphinee, WD. The circle game: understanding physician migration patterns within Canada. Acad Med. 2006 (Dec); 81(12 Suppl):S49-54. Spike NA. International medical graduates: the Australian perspective. Academic Medicine. 2006 (Sept); 81(9):842-6.

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)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0320.011

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 head0.324
GPT teacher head0.551
Teacher spread0.227 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2007
Admission routes2
Has abstractyes

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