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Record W4412769839 · doi:10.1111/tct.70170

Evaluation of a QI Module for Psychiatry Residents

2025· article· en· W4412769839 on OpenAlexaff
Kamini Vasudev, Jeffrey P. Reiss

Bibliographic record

VenueThe Clinical Teacher · 2025
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsWestern University
Fundersnot available
KeywordsSession (web analytics)CurriculumMedical educationMedicineCore competencyQuality (philosophy)Residency trainingPsychologyPsychiatryPedagogyComputer scienceContinuing education

Abstract

fetched live from OpenAlex

BACKGROUND: Quality improvement (QI) training and experience is increasingly being recognised as a mandatory core area of residency training. This was a QI initiative to develop and evaluate a QI Fundamentals teaching curriculum for psychiatry residents, in an effective and time-efficient manner. APPROACH: Using principles from the book Teaching QI in Residency Education, we developed and evaluated a workshop for first year psychiatry residents. Educational material on QI methods and tools was emailed to the residents a week prior to the 3.5-h workshop, which entailed a 50-min didactic session followed by two interactive sessions of 60 min each with both small group and large group components. EVALUATION: Effectiveness was assessed using the Self-Assessment Program (SAP), a standardised, validated tool for assessing QI competencies, over 3 consecutive academic year cohorts. The SAP evaluates the trainee's comfort level in 10 domains, regarding their current skills with various aspects of quality improvement. Twenty-three residents completed the pre- and post-SAP. There was a significant improvement in post-SAP versus pre-SAP scores in each of the 3 years in all of the 10 domains. IMPLICATIONS: The results suggest that this interactive QI workshop may be an effective and time-efficient model for imparting fundamental QI education to junior psychiatry residents and may be easily replicated by other programmes in psychiatry or other medical specialties, in particular where there are constraints in terms of time, funding and expertise in QI.

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)0.010
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.013
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 integrity0.0000.000
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 head0.163
GPT teacher head0.544
Teacher spread0.381 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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