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Record W4362522499 · doi:10.1186/s12909-023-04207-2

Fitness-for-purpose of the CanMEDS competencies for workplace-based assessment in General Practitioner’s Training: a Delphi study

2023· article· en· W4362522499 on OpenAlexfundno aff
Vasiliki Andreou, Sanne Peters, Jan Eggermont, Mieke Embo, Nele Michels, Birgitte Schoenmakers

Bibliographic record

VenueBMC Medical Education · 2023
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
FundersVlaamse regeringFonds Wetenschappelijk OnderzoekRoyal College of Physicians and Surgeons of Canada
KeywordsLikert scaleCompetence (human resources)Medical educationDelphi methodConsistency (knowledge bases)DelphiPsychologyMedicineComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: In view of the exponential use of the CanMEDS framework along with the lack of rigorous evidence about its applicability in workplace-based medical trainings, further exploring is necessary before accepting the framework as accurate and reliable competency outcomes for postgraduate medical trainings. Therefore, this study investigated whether the CanMEDS key competencies could be used, first, as outcome measures for assessing trainees' competence in the workplace, and second, as consistent outcome measures across different training settings and phases in a postgraduate General Practitioner's (GP) Training. METHODS: In a three-round web-based Delphi study, a panel of experts (n = 25-43) was asked to rate on a 5-point Likert scale whether the CanMEDS key competencies were feasible for workplace-based assessment, and whether they could be consistently assessed across different training settings and phases. Comments on each CanMEDS key competency were encouraged. Descriptive statistics of the ratings were calculated, while content analysis was used to analyse panellists' comments. RESULTS: Out of twenty-seven CanMEDS key competencies, consensus was not reached on six competencies for feasibility of assessment in the workplace, and on eleven for consistency of assessment across training settings and phases. Regarding feasibility, three out of four key competencies under the role "Leader", one out of two competencies under the role "Health Advocate", one out of four competencies under the role "Scholar", and one out of four competencies under the role "Professional" were deemed as not feasible for assessment in a workplace setting. Regarding consistency, consensus was not achieved for one out of five competencies under "Medical Expert", two out of five competencies under "Communicator",one out of three competencies under "Collaborator", one out of two under "Health Advocate", one out of four competencies under "Scholar", one out of four competencies under "Professional". No competency under the role "Leader" was deemed to be consistently assessed across training settings and phases. CONCLUSIONS: The findings indicate a mismatch between the initial intent of the CanMEDS framework and its applicability in the context of workplace-based assessment. Although the CanMEDS framework could offer starting points, further contextualization of the framework is required before implementing in workplace-based postgraduate medical trainings.

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)0.003
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.081
GPT teacher head0.431
Teacher spread0.350 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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