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Record W3176730019 · doi:10.1111/medu.14585

Competence committees: The steep climb from concept to implementation

2021· article· en· W3176730019 on OpenAlexafffundabout
Anita Acai, Nathan Cupido, Aliana Weavers, Karen Saperson, Moyez Ladhani, Sharon Cameron, Ranil Sonnadara

Bibliographic record

VenueMedical Education · 2021
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMcMaster University Medical CentreUniversity of TorontoMcMaster University
FundersCanadian Institutes of Health ResearchRoyal College of Physicians and Surgeons of Canada
KeywordsCompetence (human resources)Medical educationMandatePsychologyMedicinePolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

INTRODUCTION: Competence committees (CCs) are groups of educators tasked with reviewing resident progress throughout their training, making decisions regarding the achievement of Entrustable Professional Activities and recommendations regarding promotion and remediation. CCs have been mandated as part of competency-based medical education programmes worldwide; however, there has yet to be a thorough examination of the implementation challenges they face and how this impacts their functioning and decision-making processes. This study examined CC implementation at a Canadian institution, documenting the shared and unique challenges that CCs faced and overcame over a 3-year period. METHODS: This study consisted of three phases, which were conceptually and analytically linked using Moran-Ellis and colleagues' notion of 'following a thread.' Phase 1 examined the early perceptions and experiences of 30 key informants using a survey and semi-structured interviews. Phase 2 provided insight into CCs' operations through a survey sent to 35 CC chairs 1-year post-implementation. Phase 3 invited 20 CC members to participate in semi-structured interviews to follow up on initial themes 2 years post-implementation. Detailed observation notes from 16 CC meetings across nine disciplines were used to corroborate the findings from each phase. RESULTS: Response rates in each phase were 83% (n = 25), 43% (n = 15) and 60% (n = 12), respectively. Despite the high degree of support for CCs among faculty and resident members, several ongoing challenges were highlighted: adapting to programme size, optimising membership, engaging residents, maintaining capacity among members, sharing and aggregating data and developing a clear mandate. DISCUSSION: Findings of this study reinforce the importance of resident engagement and information sharing between disciplines. Challenges faced by CCs are discussed in relation to the existing literature to inform a better understanding of group decision-making processes in medical education. Future research could compare implementation practices across sites and explore which adaptations lead to better or worse decision-making outcomes.

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)0.000
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.480
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
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)0.0110.000

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 head0.013
GPT teacher head0.394
Teacher spread0.381 · 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 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

Citations15
Published2021
Admission routes3
Has abstractyes

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