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Record W6926161231 · doi:10.20381/ruor-31159

Patient Involvement in Teaching and Assessing Entrustable Professional Activities of Competence by Design

2025· dissertation· en· W6926161231 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Ottawa - Library · 2025
Typedissertation
Languageen
FieldEnvironmental Science
TopicIchthyology and Marine Biology
Canadian institutionsnot available
Fundersnot available
KeywordsCompetence (human resources)Interpersonal communicationPerceptionRealismQualitative researchInterpersonal relationship

Abstract

fetched live from OpenAlex

Background: Active patient involvement (i.e., patient involvement in teaching, assessment, or in roles at institutional levels) in medical education has many benefits for learners. It brings realism to education, helps learners retain information, helps learners reflect on their interactions with patients, and improves learners’ interpersonal and communication skills. Furthermore, the Royal College of Physicians and Surgeons of Canada (i.e., RCPSC; the regulating body for postgraduate medical education) endorses patient involvement in assessing residents. However, the extent of patient involvement in postgraduate medical education (PGME) in Canada is unknown. This study explores this phenomenon, namely, how (or if) patients are indicated and involved in entrustable professional activities (EPAs; sets of skills and competencies that are entrusted to a professional and compose medical education curricula) of Competence by Design (CBD), a Canadian hybrid approach of competency-based medical education. Specifically, it explores (a) how patients are indicated in a sample of RCPSC EPA documents; (b) how (or if) those involved with EPAs envisioned patients in the teaching and assessment of EPAs, during EPA creation; and (c) how (or if) patients are (or could/should be) involved in the teaching and assessment of EPAs. This study adds to the limited knowledge available on patient involvement in PGME in Canada. It illuminates medical educators’ perceptions of patient involvement in medical education and barriers to patient involvement that must be overcome for it to become reality in the teaching and assessment of EPAs and CBD overall. Methods: I conducted this study in two parts, using qualitative methods. Part 1 involved analyzing a sample of RCPSC EPA documents for how patients are indicated in them. Part 2 involved semi-structured interviews with those involved with EPAs to explore (a) how (or if) patients were discussed in the teaching and assessment of EPAs, during creation; (b) how (or if) patients could (or should) be involved in the teaching and assessment of EPAs; and (c) barriers to patient involvement in the teaching and assessment of EPAs. Findings: The findings suggest that patients are indicated in EPA documents passively compared to physicians, which minimizes opportunities for active patient involvement in the teaching and assessment of EPAs. Interviews with those involved with EPAs suggested that patients were discussed during EPA creation in terms of how patients could assess EPAs, not in terms of how patients could teach them. Those involved with EPAs believed that patients should formatively assess non-technical skills of EPAs (i.e., the cognitive, social, and personal skills that work together to contribute to quality health care and effective physician-patient interactions; Flin et al., 2008). They also believed that patients should contribute to the formative assessment of EPAs and that patients could teach non-technical aspects of EPAs through storytelling. However, participants recognized that patient involvement in the teaching and assessment of EPAs is limited due to barriers, including finding the ‘right’ patient, lack of time, risk of breaking patient anonymity, and technology. Findings of this study provide recommendations on how to improve patient involvement in the teaching and assessment of EPAs.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.196
Teacher spread0.190 · 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 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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