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Record W101414159 · doi:10.1093/pch/17.10.557

The CanMEDS role of Collaborator: How is it taught and assessed according to faculty and residents?

2012· article· en· W101414159 on OpenAlexaffabout
Elizabeth Berger, Ming‐Ka Chan, Ayelet Kuper, Mathieu Albert, Deirdre Jenkins, Megan Harrison, Ilene Harris

Bibliographic record

VenuePaediatrics & Child Health · 2012
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of OttawaUniversity of CalgaryUniversity of ManitobaUniversity of TorontoAgricultural Research Institute of OntarioThe Wilson Centre
Fundersnot available
KeywordsCurriculumConstructivist grounded theoryMedical educationFocus groupConstructivist teaching methodsGrounded theoryPsychologyMedicinePedagogyQualitative researchTeaching methodSociology

Abstract

fetched live from OpenAlex

OBJECTIVE: To explore the perspectives of paediatric residents and faculty regarding how the Collaborator role is taught and assessed. METHODS: Using a constructivist grounded theory approach, focus groups at four Canadian universities were conducted. Data were analyzed iteratively for emergent themes. RESULTS: Residents reported learning about collaboration through faculty role modelling but did not perceive that it was part of the formal curriculum. Faculty reported that they were not trained in how to effectively model this role. Both groups reported a need for training in conflict management, particularly as it applies to intraprofessional (physician-to-physician) relationships. Finally, the participants asserted that current methods to assess residents on their performance as collaborators are suboptimal. CONCLUSIONS: The Collaborator role should be a formal part of the residency curriculum. Residents need to be better educated with regard to managing conflict and handling intraprofessional relationships. Finally, innovative methods of assessing residents on this non-medical expert role need to be created.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
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 head0.030
GPT teacher head0.417
Teacher spread0.388 · 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

Citations25
Published2012
Admission routes2
Has abstractyes

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