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Record W2329897407 · doi:10.1097/sap.0b013e318257380b

International Plastic Surgery Missions

2012· article· en· W2329897407 on OpenAlexaffabout
Colin White, Catherine Lecours, Patricia Bortoluzzi, Louise Caouette‐Laberge, Yvonne Ying

Bibliographic record

VenueAnnals of Plastic Surgery · 2012
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicineTRIPS architectureMedical educationGraduate medical educationHealth carePatient careChinaPlastic surgeryResidency trainingSurgeryNursingContinuing educationAccreditation

Abstract

fetched live from OpenAlex

Residency education has shifted over the past decade in an attempt to graduate well-rounded physicians. There is a recognition that a physician's abilities must extend beyond medical knowledge. The Royal College of Physicians and Surgeons of Canada introduced the CanMEDS physician competency framework in 2005. The framework provides 7 areas of competencies that are aimed at providing improved patient care. These competencies are medical expert, communicator, collaborator, manager, health advocate, scholar, and professional. Teaching and evaluating many of these competencies is often challenging for residency training programs. We believe that international surgical missions provide a prime opportunity to teach and evaluate all CanMEDS' roles.Plastic surgery is a field with many different organizations involved in international surgery. Many plastic surgery training programs offer opportunities for residents to become involved in these international surgical missions. Through these trips, residents gain surgical experience, see a variety and volume of clinical cases, and have the opportunity to travel to a foreign country and experience different cultures. We believe that international plastic surgery surgical missions also provide an exceptional micro environment for the teaching of CanMEDS roles. Using examples from residents' personal experiences on international plastic surgery missions to China, Mali, and Cambodia, we describe the benefits of these missions in transferring the CanMEDS competencies to resident training.

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)0.001
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.110
GPT teacher head0.360
Teacher spread0.250 · 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

Citations8
Published2012
Admission routes2
Has abstractyes

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