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Record W3010131754 · doi:10.1186/s12960-020-0447-4

The impact of a global health elective on CanMEDS competencies and future practice

2020· article· en· W3010131754 on OpenAlexaff
Ashley Lanys, Gena Krikler, Rachel F. Spitzer

Bibliographic record

VenueHuman Resources for Health · 2020
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsMount Sinai HospitalWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsGlobal healthMedicineMedical educationGlobal LeadershipHealth services researchNursingPsychologyFamily medicinePublic healthPublic relationsPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: There is evidence that participating in global health electives generates positive educational outcomes and personal benefits for medical trainees. The objective of this study was to examine the effect and impact that a global health elective has on CanMEDS competencies and anticipated future practice. RESULTS: The medical expert, collaborator, leader, scholar, and professional CanMEDS competencies were self-perceived to be strongly impacted through this elective. A total of 94% of participants indicated it increased their strengths as a medical expert and leader, 82% indicated a major impact on the scholar competency, 88% of participants reported a strong impact as a professional, and 76% of participants indicated that it strongly impacted them as a collaborator. The majority of participants continue to have involvement in global health, and 88% of respondents found this elective to be influential on their current practice and beliefs. CONCLUSIONS: These results suggest that individuals who participated in this global health elective perceived value in their experience. These findings support our hypothesis that participation in this global health elective would generate self-perceived positive impacts. Global health electives may provide an opportunity for physicians to expand on their CanMEDS competencies and become more proficient in caring for diverse patient populations.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.396
Teacher spread0.370 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2020
Admission routes1
Has abstractyes

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