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Record W2321306460 · doi:10.15766/mep_2374-8265.9086

The UCSF Faculty Development Workshop on Critical Reflection in Medical Education: Training Educators to Teach and Provide Feedback on Learners' Reflections

2012· article· en· W2321306460 on OpenAlexaboutno aff
Louise Aronson, Marieke Kruidering, Patricia O’Sullivan

Bibliographic record

VenueMedEdPORTAL · 2012
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumReflection (computer programming)Critical reflectionMedical educationSet (abstract data type)PsychologyMathematics educationPedagogyLibrary scienceComputer scienceMedicine

Abstract

fetched live from OpenAlex

Abstract This guide describes a literature-derived workshop to train faculty in the skill of critical reflection and to teach them to foster the skill in their learners. The basic workshop lasts two hours, and we provide options for two different three hour workshops and one four hour long workshop as well. All versions of the workshop encourage participant interaction throughout through the use of multiple teaching modalities: individual, small and large group exercises; use of written materials, white board/flip charts and PowerPoint. Participants apply their skill as educators to analyze more and less effective reflections, are introduced to the concepts and data surrounding critical reflection in medical education and a structured approach for promoting effective and educational reflection, practice giving feedback on critical reflections. In the longer workshops, they may also write critical reflections themselves, gain additional feedback experience, and draft approaches to incorporating reflection in the courses and curricula to assess competencies. Materials include: a PowerPoint slide set; two sets of sample reflections (5 total), each with an annotated presenter's guide; a sheet for rating and evaluating sample reflection quality; the UCSF LEaP Guide, a structured approach to critical reflection; guidelines for providing feedback on both content and reflective skill of critical reflections; a prompt for faculty critical reflection; and a workshop evaluation form. As of August 2011, the authors had given this workshop 19 times, receiving consistently high ratings (mean overall rating 4.6 on a 5 point scale), moderately high rates of self-efficacy (4.2 out of 5), and leading to substantial implementation of critical reflection by trained faculty (74%) and broad dissemination locally and at other institutions across the United States. At UCSF, the impact of the workshop has included greater teaching of critical reflection throughout the medical curriculum (e.g. in pre-clerkship lab exercises for first year medical students, in many core clerkships, and in the medical student portfolios for competency milestone assessment; in residencies from Emergency Medicine to Psychiatry and in several fellowships), greater consistency in using reflection and critical reflection accurately and appropriately, coordination of reflective skills training across courses and clerkships, and more consistent feedback to learners on their reflective exercises. Participants from other institutions, including Stanford, the University of Colorado, FSU, Hofstra, and the University of Toronto, who took the workshop at regional and national meetings have contacted us and indicated that they have adopted our approach to critical reflection. Importantly, while there have been brief reports about faculty training in reflection, we have not found other workshops based on the literature and extensive medical education experience which have been thoroughly described, broadly disseminated, and evaluated across learner levels and institutions (paper in progress) as this one has.

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)0.022
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.022
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.003
Scholarly communication0.0030.002
Open science0.0030.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0180.012

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 head0.076
GPT teacher head0.448
Teacher spread0.372 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations3
Published2012
Admission routes1
Has abstractyes

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