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Record W2965802270 · doi:10.1097/acm.0000000000002896

Attitudes Towards Physicians Requiring Remediation: One-of-Us or Not-Like-Us?

2019· article· en· W2965802270 on OpenAlexaffabout
Gisèle Bourgeois‐Law, Pim W. Teunissen, Lara Varpio, Glenn Regehr

Bibliographic record

VenueAcademic Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAmbivalenceMedical educationPerceptionFocus groupPsychologyPublic relationsTask (project management)NursingMedicineSocial psychologyPolitical scienceSociology

Abstract

fetched live from OpenAlex

PURPOSE: The data for this paper were collected as part of a larger project exploring how the medical profession conceptualizes the task of supporting physicians struggling with clinical competency issues. In this paper, the authors focus on a topic that has been absent in the literature thus far-how physicians requiring remediation are perceived by those responsible for organizing remediation and by their peers in general. METHOD: Using a constructivist grounded theory approach, the authors conducted semistructured interviews with 17 remediation stakeholders across Canada. Given that in Canada health is a provincial responsibility, the authors purposively sampled stakeholders from across provincial and language borders and across the full range of organizations that could be considered as participating in the remediation of practicing physicians. RESULTS: Interviewees expressed mixed, sometimes contradictory, emotions toward and perceptions of physicians requiring remediation. They also noted that their colleagues, including physicians in training, were not always sympathetic to their struggling peers. CONCLUSIONS: The medical profession's attitude toward those who struggle with clinical competency-as individuals and as a whole-is ambivalent at best. This ambivalence grows out of psychological and cultural factors and may be an undiscussed factor in the profession's struggle to deal adequately with underperforming members. To contend with the challenge of remediating practicing physicians, the profession needs to address this ambivalence and its underlying causes.

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)0.009
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.006
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.056
GPT teacher head0.384
Teacher spread0.328 · 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 designQualitative
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

Citations16
Published2019
Admission routes2
Has abstractyes

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