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Record W2793147298 · doi:10.1097/acm.0000000000002169

A Program in Transition: A Shifting Mind-set

2018· letter· en· W2793147298 on OpenAlexaffabout
Alanna Wong, Niran Argintaru

Bibliographic record

VenueAcademic Medicine · 2018
Typeletter
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsRoyal Ontario Museum
Fundersnot available
KeywordsCurriculumFeelingSet (abstract data type)ConstructiveMedical educationProcess (computing)PsychologyComputer scienceMedicinePedagogySocial psychology

Abstract

fetched live from OpenAlex

To the Editor: Across Canada, residency programs are transitioning to competency-based medical education (CBME). In brief, CBME is an outcomes-based educational approach that will change the design, implementation, assessment, and evaluation of residency programs.1 Over the past year, we have provided a resident’s perspective for the redesign of our program’s curriculum as it transitions to CBME. In planning for the transition progresses, a challenging source of discussion has been how to facilitate the culture shift necessary for CBME-based learning and assessment. How do residents dissociate not being deemed competent from feeling a sense of personal failure? Learners and educators already find feedback challenging. Negative feedback is often viewed as failure, even when intended as constructive. In CBME, residents will aim to become “competent” in preestablished milestones. Yet, the expectation is that for most skills, residents will not achieve competency until later in their training. For CBME to succeed, residents must transition from perceiving feedback as a high-stakes process to, instead, viewing it as a continuous and essential part of achieving competency. Learners will have to adjust their expectations, and approach assessments as opportunities for growth rather than notifications of failure. The intention of the CBME framework, which relies on many low-stakes assessments, is that learners will use these assessments as opportunities to identify weaknesses, set learning goals, and then receive the tools needed to facilitate their learning. Educators will have to emphasize that specific feedback is not meant as a global reflection of a learner and be cognizant that feedback be given in a safe environment since “receiving feedback sits at the intersection of these two needs—our drive to learn and our longing for acceptance.”2 The opportunity to be involved in the CBME transition process has spurred us toward becoming intentional about our own learning, thoughtful of knowledge gaps, and inquisitive of feedback—accepting it modestly while being open to our own vulnerability. The transition to CBME is ongoing, and as it progresses, critical self-reflection by learners and educators alike to adapt their mind-sets and maintain a positive learning environment is essential for the success of not only trainees but also residency programs. Alanna Wong, MDSecond-year resident, University of Toronto Medicine, Royal College Emergency Resident Medicine Program, Toronto, Ontario, Canada; ORCID: https://orcid.org/0000-0003-2421-5017. Niran Argintaru, MDFourth-year resident, University of Toronto Medicine, Royal College Emergency Resident Medicine Program, Toronto, Ontario, Canada; [email protected]; Twitter: @EMNiran; ORCID: http://orcid.org/0000-0002-4029-1231. First published online February 13, 2018

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)0.045
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.017
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.045
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0060.008
Open science0.0040.003
Research integrity0.0170.036
Insufficient payload (model declined to judge)0.0080.003

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 head0.055
GPT teacher head0.404
Teacher spread0.349 · 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
GenreCommentary

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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Citations2
Published2018
Admission routes2
Has abstractyes

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