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Outcomes of Using Peer‐Mentors and Bi‐Directional Development of CanMEDS Core Competencies at a Distributed Medical Education Site

2016· article· en· W4389024588 on OpenAlexaffabout
Michael T Pignanelli, Jonathan Williams, Christopher Hillman, Sandra Mekhaiel, Anna Farias

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMedical educationCore competencyCurriculumPortfolioPeer feedbackPsychologyMedicinePedagogyManagement

Abstract

fetched live from OpenAlex

INTRODUCTION Due to the growing portfolio of skills needed to succeed as doctors, time and resources allocated by medical schools in Canada to teach anatomy have been greatly reduced and redirected towards other topics. Operating a distributed education campus requires even greater austerity and has the potential to hinder learner acquisition of key medical competencies. Studies in the past have looked at using peer approaches to deal with similar problems in medical education in the anatomy lab. Near peer‐mentoring (NPM), defined as the guidance of less experienced colleagues by colleagues with more experience, was implemented at the distributed education branch of the Schulich School of Medicine & Dentistry in Windsor, Ontario. This paper explores how NPM impacts peer learning and core medical competencies in both learners and teaching assistants (TAs). Some of the CanMED core competencies explored in this study are Medical Expert, Communicators, Collaborators, Leaders, Scholars and Professionals. METHODS Second year medical students volunteered to be TAs for anatomy labs. TAs were responsible for providing feedback on lesson plans, guiding students through lab dissections and answering questions on content. Implementing near‐peer TAs improved teacher to learner ratio to a high degree. Four near‐peer TAs, provided reflections on acquisition of CanMEDs medical competencies through NPM. TA involvement varied depending on the demands of the 1st year curriculum, ranging from 2–8 hours per week during teaching sessions and 1–2 hours of preparation time per lab, per TA. An initial survey was administered to student learners to qualitatively evaluate their current experience in the anatomy lab. A follow‐up survey will be distributed at the end of the final anatomy session. Finally, the faculty anatomist provided a reflection on how the near‐peer mentors helped deliver the anatomy instruction at the distributed education site. RESULTS Twenty‐six learners of thirty‐eight first year learners completed the initial survey. On average, the learners indicated that Medical Expert was their most improved competency during anatomy labs. Learners felt they improved the least on the Leadership competency during anatomy labs. When evaluating TAs, learners ranked the Communicator and Professional competencies the highest. TAs found the near‐peer program to be a meaningful opportunity to integrate and apply multiple competencies at once. Upon reflection, TAs thought that the Medical Expert, Communicator and Leader competencies were most used. In order to improve delivery of anatomy labs in the future, TAs thought more time should be dedicated to understanding the details in delivering the lab session. The near‐peer TA program was felt to improve access to help in learner's critical thinking skills. Further, faculty noted TA development of professional skills including leadership, communication and being collaborators. CONCLUSIONS Teaching Assistants had a positive overall impact on learners experience in anatomy lab. TAs themselves felt near‐peer mentoring to be an emotionally impactful opportunity to practice integrating the CanMed core competencies such as medical expertise, communication and leadership.

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)0.006
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.033
GPT teacher head0.332
Teacher spread0.299 · 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 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
Published2016
Admission routes2
Has abstractyes

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