Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".