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Record W4297995440 · doi:10.37964/cr24760

Development of an institutional “good practices” policy for resident and attending-physician on-call responsibilities

2022· article· en· W4297995440 on OpenAlexvenueno aff
Matthew Lipinski, Shahbaz Syed, Jerry M Maniate

Bibliographic record

VenueCanadian Journal of Physician Leadership · 2022
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsOperationalizationGuidelineQuality (philosophy)Public relationsFocus groupQuality managementPsychologyBusinessMedicineMedical educationNursingFamily medicinePolitical scienceMarketing

Abstract

fetched live from OpenAlex

On-call coverage by resident physicians is common in academic hospitals, but the interaction between residents and supervising attending physicians varies. Responsibilities are often not clearly defined, which contributes to unclear expectations on the part of both. We developed an institutional “on-call responsibilities” guideline for both residents and attending physicians using a nominal group technique to gain consensus with multiple institutional stakeholders. Three focus groups engaged 31 clinical stakeholders in the development of concise guidelines that include 12 resident responsibilities and 12 attending-physician responsibilities that can be implemented while on-call. Using the nominal group technique allowed us to engage a large number of stakeholders and generate a robust guideline that could be easily operationalized to create a consistent expectation of responsibilities while on call, promote patient safety. It can also potentially reduce resident burnout. This quality-improvement project generated a list of concrete responsibilities that can be used in other centres and provides a robust approach to developing similar policies in other clinical contexts.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.126
GPT teacher head0.365
Teacher spread0.239 · 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 teacher head, 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

Citations0
Published2022
Admission routes1
Has abstractyes

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