MétaCan
Menu
Back to cohort
Record W2107953432 · doi:10.3109/01421591003690346

Quality education: A pilot quality improvement curriculum for psychiatry residents

2010· article· en· W2107953432 on OpenAlexaffabout
Sanjeev Sockalingam, Vicky Stergiopoulos, Julie Maggi, Ari Zaretsky

Bibliographic record

VenueMedical Teacher · 2010
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsSt. Michael's HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsCurriculumMedical educationExperiential learningFocus groupMedicineWorkloadQuality (philosophy)PsychologyPedagogyManagement

Abstract

fetched live from OpenAlex

BACKGROUND: A series of Institute of Medicine's reports have highlighted the need for greater quality improvement (QI) training in medical education; however, few formal QI curricula for medical trainees have been described in the literature. AIM: The objective of this study was to develop a contextual QI curriculum involving a QI workshop and longitudinal QI projects (QIPs) for psychiatry trainees. METHODS: We examined psychiatry residents' attitudes on QI training following their exposure to a physician-manager curriculum using focus group methodology. Focus group data were used to inform revisions to the QI curriculum. Following the curriculum revisions, we administered a resident questionnaire to elicit resident perceptions on the modified QI curriculum. RESULTS: Focus group data from 40 psychiatry residents at the University of Toronto identified the following themes: challenges with QIP workload, difficulties of QI workshop integration into the curriculum, and value of the experiential component of the QIP. Of the 26 residents, 18 completed the resident questionnaire on the revised curriculum and reported an enhanced appreciation of QI in their current clinical practice. CONCLUSION: The study results suggest that this experiential format warrants further exploration as a model for QI training in medicine.

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)0.004
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.262
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.034
GPT teacher head0.440
Teacher spread0.406 · 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.

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

Citations24
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueMedical TeacherSame topicInnovations in Medical EducationFrench-language works237,207