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Record W2433241392 · doi:10.1097/acm.0000000000000727

In Reply to Weissman

2015· letter· en· W2433241392 on OpenAlexaffabout
Richard L. Cruess, Sylvia R. Cruess, J. Donald Boudreau, Linda Snell, Yvonne Steinert

Bibliographic record

VenueAcademic Medicine · 2015
Typeletter
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsMentorshipMandateIdentity (music)Perspective (graphical)PsychologyAffect (linguistics)Space (punctuation)Medical educationProfessional responsibilityMedicinePolitical scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

We are pleased to reply to Dr. Weissman, who highlights an important issue of interest—the widely accepted fact that learners are frequently exposed to unprofessional behavior on the part of their clinical teachers. He suggests solving this problem by establishing an ombudsman for students. The ombudsman would be mandated to “explore with the student any clinical experiences that the student believes are not within the boundaries of appropriate professional behavior.” We do not disagree with Dr. Weissman’s highlighting the potential impact of negative role modeling on identity formation. His proposed solution is one of many that could prove beneficial. However, to put the issue in perspective, our article points out that, although clinical experiences and role models are of paramount importance in professional identity formation (PIF), there are other factors that contribute to the learning environment. Thus, if an ombudsman is to be responsible for assisting students to develop their professional identities, the mandate must be broadened to include the many personal and institutional factors that affect PIF.1 The appointment of an ombudsman is only one possible solution. Some institutions assign responsibility for creating a safe learning environment to medical school faculty. Others, such as ours, rely heavily on mentorship programs to provide a safe space for learners. McGill University assigns each student a mentor (called an Osler Fellow) at the beginning of medical school, and each mentor, along with a more senior student, follows six students throughout their four-year course of study.2 Finally, Dr. Weissman asserts that students must see that unprofessional behavior “will be appropriately disciplined.” If the emphasis is to be on discipline, a system of assessing faculty professionalism that is valid and reliable must be present. As the intent is to assist students to develop their professional identities, structured student input into this system is essential. Such tools are available.3 Supporting PIF is an important objective that requires an integrated educational program as well as broad-based faculty support. An ombudsman could well be a valuable part of this effort but, alone, would not be sufficient. Richard L. Cruess, MD Professor of surgery and core faculty member, Center for Medical Education, McGill University Faculty of Medicine, Montreal, Quebec, Canada; [email protected] Sylvia R. Cruess, MD Professor of medicine and core faculty member, Center for Medical Education, McGill University Faculty of Medicine, Montreal, Quebec, Canada. J. Donald Boudreau, MD Associate professor of medicine and core faculty member, Center for Medical Education, McGill University Faculty of Medicine, Montreal, Quebec, Canada. Linda Snell, MD, MHPE Professor of medicine and core faculty member, Center for Medical Education, McGill University Faculty of Medicine, Montreal, Quebec, Canada. Yvonne Steinert, PhD Professor of family medicine and director, Center for Medical Education, McGill University Faculty of Medicine, Montreal, Quebec, Canada.

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)0.004
metaresearch head score (Gemma)0.054
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 score0.018
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.054
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0050.007
Open science0.0030.003
Research integrity0.0180.041
Insufficient payload (model declined to judge)0.0100.008

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 head0.059
GPT teacher head0.403
Teacher spread0.345 · 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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Citations5
Published2015
Admission routes2
Has abstractyes

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