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Education in Quality of Care in an Internal Medicine Residency Program

2001· article· en· W2024139603 on OpenAlexaff
Donald Farquhar, Kathryn Myers, Derek Benjamin

Bibliographic record

VenueAcademic Medicine · 2001
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsKingston General HospitalQueen's University
Fundersnot available
KeywordsQuality assuranceQuality (philosophy)CurriculumQuality managementMedical educationMedicineHealth careSession (web analytics)PsychologyFamily medicineComputer scienceOperations managementManagement system

Abstract

fetched live from OpenAlex

Objective: Because resident physicians usually work at the “front lines” of care, they are ideally situated to become active in quality assurance and improvement activities in their institutions. We developed and pilot tested a curriculum designed to allow residents in our internal medicine residency program to learn and apply key concepts in the assessment and improvement of the quality of the care that they deliver at our institution. Description: We launched our program in 1999 with an interactive half-day seminar in which we presented an overview of the core curricular content. Terms such as quality of care, quality control, quality assurance, and quality improvement were defined. Concepts such as the technical and interpersonal dimensions of care, small-area variation in care, and the structure-process-outcome paradigm of health care quality were introduced. Tools used in the measurement and enhancement of quality were illustrated through case discussion and review of selected abstracts from the literature on quality of care. These included mortality and morbidity review, peer review, examination of critical incidents, medical audit, and methods in total quality management. The introductory seminar was followed by a series of monthly noon-hour sessions devoted to group review of selected episodes of care in which suboptimal quality had been identified. These sessions were organized by a resident peer leader, who presented the case scenarios in anonymous fashion and led the resident group through an examination of the processes and outcomes of the care delivered, and a discussion of how care might have been improved. At each session, selected aspects of the curricular content, introduced at the initial half-day seminar, were reviewed in the context of the case discussions. The residents were thus able to use these discussions as an opportunity to identify, in a constructive and nonthreatening fashion, both system-embedded problems and the gaps in knowledge, skills, or attitudes of the caregivers that might have contributed to suboptimal quality. The residents' knowledge of concepts in quality of care was demonstrated, using a pre-test (administered at the outset of the introductory seminar) and a post-test (administered at the conclusion of the last noon-hour session of the year), to have improved over the course of the year. Discussion: The residents responded favorably to the introduction of this seminar series into their curriculum. The case-discussion format allowed them to learn and apply concepts that previously they might have perceived as dry, mundane, disconnected from their everyday work, or even threatening. The residents also found that their intimate knowledge of hospital-based processes of care gave them insight into problems that were attributable to system, rather than individual, performance. Identification of such system problems through group discussion also served to stimulate their interest in seeking system-based solutions. Key to the success of this series were the involvement of a resident peer-leader from conceptual stage through implementation and evaluation, and the support of faculty members with interest and training in quality improvement methods.

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)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.057
GPT teacher head0.508
Teacher spread0.452 · 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 designObservational
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

Citations2
Published2001
Admission routes1
Has abstractyes

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