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Record W4411348721 · doi:10.4300/jgme-d-24-00814.1

Interactive Teaching Strategies for Accreditation Success: Insights From a Canadian Residency Program

2025· article· en· W4411348721 on OpenAlexafffundabout
Tessa Hanmore, Allie Singers

Bibliographic record

VenueJournal of Graduate Medical Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsQueen's University
FundersQueen's University
KeywordsAccreditationMedical educationGraduate medical educationMEDLINEData scienceComputer scienceMedicinePolitical science

Abstract

fetched live from OpenAlex

In Canada, all postgraduate medical education programs are required to undergo regular accreditation reviews conducted by the Canadian Residency Accreditation Consortium. These reviews can result in accreditation statuses of “Accredited Program” and “Accredited Program on Notice of Intent to Withdraw Accreditation.” Programs placed on Notice of Intent to Withdraw Accreditation must undergo an external review within 24 months, risking the loss of accreditation if they fail to meet standards.This was the situation faced by Queen’s University’s Department of Child and Adolescent Psychiatry residency program, a small program in Kingston with 2 to 3 residents and 11 associated faculty/allied health professions. During their regular accreditation review, the program was placed on notice of intent to withdraw, necessitating thorough preparation for the upcoming external review.The leadership team recognized the need to prepare all stakeholders effectively. Given the small size of the program, it was expected that most stakeholders were already familiar with the relevant information. However, the leadership team wanted to ensure comprehensive preparation without redundancy.Previously, preparation for internal reviews involved lecture-based sessions lasting approximately one hour, led by the Educational Consultant with support from the leadership team. These sessions, while informative, were perceived as repetitive by stakeholders who were already knowledgeable about the content.To address this, the leadership team decided to revise the format of the teaching sessions. They introduced an interactive, question-based approach to present the material. This intervention aimed to refresh known information, introduce new knowledge, and verify stakeholders’ understanding efficiently. Although active learning is not a new pedagogy, in the experience of the leadership team, using it as an engagement strategy for faculty virtual learning has not been widely adopted.The new format involved creating specific questions tailored to each stakeholder group. See the Table for examples of questions.This approach allowed for a more dynamic and engaging learning experience. If stakeholders knew the answers, the session moved on quickly, saving time for areas where knowledge gaps existed. The sessions concluded earlier than planned, demonstrating time efficiency. Additionally, contentious topics were expanded upon regardless of responses to ensure thorough understanding. The time investment for the leadership team in the development and planning of this session was less than creating a standard PowerPoint.Post-session, questions with answer keys and links to source documents were distributed to reinforce learning. Feedback from stakeholders was overwhelmingly positive, highlighting the effectiveness of the format and content. Participants felt better prepared for the external surveyors’ questions and appreciated the opportunity for discussion and active participation.The program performed exceptionally well in the external accreditation review, with all stakeholders demonstrating preparedness and confidence. The leadership team’s innovative teaching strategy not only ensured compliance with accreditation standards but also fostered a collaborative and informed community within the residency program.The Child and Adolescent Psychiatry program at Queen’s University will continue to use this method to present faculty and learners with information. Although this innovation took place at only one program in one institution, the authors believe that with adjustments to the questions it can easily be adapted and used at other institutions and in other specialities.

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)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.474
Teacher spread0.435 · 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 designTheoretical or conceptual
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

Citations0
Published2025
Admission routes3
Has abstractyes

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