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Record W4223483785 · doi:10.1186/s12909-022-03308-8

An online Delphi study to investigate the completeness of the CanMEDS Roles and the relevance, formulation, and measurability of their key competencies within eight healthcare disciplines in Flanders

2022· article· en· W4223483785 on OpenAlexfundno aff
Oona Janssens, Mieke Embo, Martín Valcke, Leen Haerens

Bibliographic record

VenueBMC Medical Education · 2022
Typearticle
Languageen
FieldPsychology
TopicCompetency Development and Evaluation
Canadian institutionsnot available
FundersFonds Wetenschappelijk OnderzoekRoyal College of Physicians and Surgeons of Canada
KeywordsRelevance (law)Delphi methodKey (lock)Health careDelphiMedical educationCompleteness (order theory)PsychologyEngineering ethicsComputer scienceMedicinePolitical scienceEngineeringMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: Several competency frameworks are being developed to support competency-based education (CBE). In medical education, extensive literature exists about validated competency frameworks for example, the CanMEDS competency framework. In contrast, comparable literature is limited in nursing, midwifery, and allied health disciplines. Therefore, this study aims to investigate (1) the completeness of the CanMEDS Roles, and (2) the relevance, formulation, and measurability of the CanMEDS key competencies in nursing, midwifery, and allied health disciplines. If the competency framework is validated in different educational programs, opportunities to support CBE and interprofessional education/collaboration can be created. METHODS: A three-round online Delphi study was conducted with respectively 42, 37, and 35 experts rating the Roles (n = 7) and key competencies (n = 27). These experts came from non-university healthcare disciplines in Flanders (Belgium): audiology, dental hygiene, midwifery, nursing, occupational therapy, podiatry, and speech therapy. Experts answered with yes/no (Roles) or on a Likert-type scale (key competencies). Agreement percentages were analyzed quantitatively whereby consensus was attained when 70% or more of the experts scored positively. In round one, experts could also add remarks which were qualitatively analyzed using inductive content analysis. RESULTS: After round one, there was consensus about the completeness of all the Roles, the relevance of 25, the formulation of 24, and the measurability of eight key competencies. Afterwards, key competencies were clarified or modified based on experts' remarks by adding context-specific information and acknowledging the developmental aspect of key competencies. After round two, no additional key competencies were validated for the relevance criterion, two additional key competencies were validated for the formulation criterion, and 16 additional key competencies were validated for the measurability criterion. After adding enabling competencies in round three, consensus was reached about the measurability of one additional key competency resulting in the validation of the complete CanMEDS competency framework except for the measurability of two key competencies. CONCLUSIONS: The CanMEDS competency framework can be seen as a grounding for competency-based healthcare education. Future research could build on the findings and focus on validating the enabling competencies in nursing, midwifery, and allied health disciplines possibly improving the measurability of key competencies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.041
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.215

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.042
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0050.003
Scholarly communication0.0020.002
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.080
GPT teacher head0.364
Teacher spread0.284 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations24
Published2022
Admission routes1
Has abstractyes

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