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Record W2332467280 · doi:10.1097/acm.0000000000000510

Safety in Numbers

2014· letter· en· W2332467280 on OpenAlexaffabout
Ghazwan Altabbaa, Tanya Beran, Alyshah Kaba

Bibliographic record

VenueAcademic Medicine · 2014
Typeletter
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPollingAnonymitySession (web analytics)PsychologyMedical educationGatekeepingMedicineSocial psychologyPublic relationsLaw

Abstract

fetched live from OpenAlex

To the Editor: Ginsburg and colleagues1 touch the tip of the iceberg in their recent article examining physicians’ responses to professional challenges. The authors analyzed physicians’ explanations of why they would make “wrong” decisions in response to dilemmas likely to occur in medical practice and found that participants’ desire to be helpful was a frequent rationale. If being seen as helpful requires decisions that are inconsistent with standard of care guidelines, perhaps we should question the usefulness of such guidelines. Of equal importance, yet not emphasized by Ginsburg and colleagues (because it was not as prominent as other factors they identified), is the role of social pressure as an influence on physicians’ decision making. The physicians in this study referred to the actions of other doctors when making decisions about dilemmas—as Ginsburg and colleagues note, “Participants often made reference to ‘what the other doctors’ would do, even while admitting that they do not always know what other doctors would actually do.” To use other doctors as reasons for less-than-professional practice seems, well, unprofessional. These subtle social pressures occur far more often in everyday practice. For example, we recently attended a medical grand rounds session, during which a respected faculty member shared a patient history with an unequivocal diagnosis and management plan. However, in the interest of seeing how the audience would respond, he gave a “wrong” diagnosis. This was followed by a series of three questions posed to the audience and an invitation to respond by using polling clickers for anonymity. The polling percentages showed the audience’s alignment with the incorrect diagnosis, with 48% choosing a diagnostic step, 30% choosing a first line of therapy, and 62% choosing a disposition and consultation that would be consistent with the incorrect diagnosis. Are physicians using one another as a safety net or as a means of absolving personal responsibility? There are many similar anecdotes, and empirical studies are emerging too.2 With the goal of evolving into a high-reliability organization driven by a focus on patient safety and high-quality decision making, findings such as those reported by Ginsburg and colleagues demonstrate that justifying and rationalizing a mind-set for conforming to the need to be seen as helpful to our patients, friends, colleagues, and the general public is not helpful at all. Ghazwan Altabbaa, MD, MSc Clinical associate professor and associate program director, Internal Medicine Residency Program, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; [email protected] Tanya Beran, PhD Professor of medical education, Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. Alyshah Kaba, MSc PhD candidate, medical education and research, Department of Community Health Sciences, Cumming School of Medicine, W21C Research and Innovation Centre, University of Calgary, Calgary, Alberta, 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)0.006
metaresearch head score (Gemma)0.063
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: none
Teacher disagreement score0.204
Threshold uncertainty score0.683

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.063
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0080.006
Open science0.0040.003
Research integrity0.0130.014
Insufficient payload (model declined to judge)0.2040.119

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 head0.044
GPT teacher head0.370
Teacher spread0.326 · 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

Citations4
Published2014
Admission routes2
Has abstractyes

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