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Record W2768144927 · doi:10.1097/acm.0000000000001980

In Reply to Hyder

2017· letter· en· W2768144927 on OpenAlexaffabout
Asim Alam, Jessica J. Lui, Chaim M. Bell

Bibliographic record

VenueAcademic Medicine · 2017
Typeletter
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health NetworkSunnybrook Health Science CentreHealth Sciences CentreMount Sinai Hospital
Fundersnot available
KeywordsGraduation (instrument)DisciplineWorkloadCohortSubject (documents)PsychologyMedical educationMedicinePolitical scienceLawManagementPathology

Abstract

fetched live from OpenAlex

We thank our colleague, Sadia Hyder, for commenting on our article published in a previous issue of the journal.1 We recognize her viewpoint that our discussion did not exhaust all the possible explanations for the increased rate of discipline amongst international medical graduates (IMGs) compared with North American medical graduates. Instead, our article serves as a primer on the subject—we hope that it will lead to more research endeavors in this field of patient safety. We continue to caution all readers against assuming that our proposed hypotheses are causative. However, we would challenge the hypothesis that transition periods are an important factor in disciplinary issues among IMGs. Interestingly, we found that most physicians in our cohort were disciplined an average of 32.8 years after graduating medical school.1 In other words, these physicians were disciplined at later stages in their careers. Although definitive data are unknown, we believe that a small proportion of IMGs start to practice medicine after 30 years of graduation. In this manner, the majority of the disciplined IMGs in our cohort had more than sufficient time in which to acclimatize to Canadian laws, values, and social norms prior to their disciplinary action. While it is certainly true that a heavy workload leading to physical and cognitive exhaustion may contribute to an increased risk for discipline, we are not convinced that this is more of an issue for individuals who trained outside of Canada but, rather, one that is faced by all physicians from all types of backgrounds. Nonetheless, we agree that mentorship, teaching, and a multitude of other guidance opportunities should be available to all physicians throughout their careers to potentially mitigate the occurrence of certain types of discipline. Still, there are no data to support that these types of programs have any effect on preventing disciplinary action. The interpretation of our data is cautious because the data objectively point to a simple association: Physicians who completed medical education outside of Canada had increased rates of discipline compared with domestically trained physicians. As mentioned above, our results provide a starting point for a discussion of why this phenomenon is observed. Preventing physician discipline is of utmost importance to improve patient safety; all physicians must take ownership and focus more attention upon this topic. We welcome other colleagues, including Dr. Hyder, to conduct further research into the complexities on why our findings have occurred. Asim Alam, MDStaff anesthesiologist and transfusion medicine specialist, Department of Anesthesia, Sunnybrook Health Sciences Centre, and Department of Anesthesia, University of Toronto, Toronto, Ontario; contact at Sunnybrook Health Sciences Centre, Department of Anesthesia, 2075 Bayview Ave., Rm M3-200, Toronto, Ontario, M5G 1X5, Canada. Jessica J. Lui, MD, MScGeneral medical internist, Division of General Internal Medicine, University Health Network and Department of Medicine, University of Toronto, Ontario, Canada. Chaim M. Bell, MD, PhDDeputy physician-in-chief, Division of General Internal Medicine, Mt. Sinai Hospital and Department of Medicine, University of Toronto, Toronto, Ontario, 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)0.008
metaresearch head score (Gemma)0.091
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 score0.029
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.091
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0040.006
Scholarly communication0.0060.010
Open science0.0040.004
Research integrity0.0290.065
Insufficient payload (model declined to judge)0.0140.009

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 head0.122
GPT teacher head0.522
Teacher spread0.400 · 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".

Quick stats

Citations0
Published2017
Admission routes2
Has abstractyes

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