Content and process: using continuous quality improvement to teach and evaluate learning outcomes in quality improvement residency education
Bibliographic record
Abstract
BACKGROUND: Psychiatry has not prioritised quality improvement and patient safety (QIPS) to the same degree as other medical specialties. Professional capacity building in QIPS through the education of residents is essential to improving the quality and safety of mental healthcare delivery. LOCAL PROBLEM: The University of Toronto postgraduate psychiatry program is the largest psychiatry training program in North America. Training in QIPS was introduced in 2006. In 2019, a curricular review found that few trainees acquired competence in QIPS. METHODS: Curricular change was undertaken using Kern's Six-Step Approach to curricular design. We used a continuous quality improvement framework to inform the evaluation with data collection using an online educational application. We aimed to improve competence in QIPS as demonstrated by assessment of the quality of individual quality improvement projects (IQIP) on an 11-item rubric. We used a family of quality improvement measures to iteratively improve the curriculum over 3 years. INTERVENTIONS: We restructured the QIPS curriculum into four case-based seminars for third year psychiatry residents. The curriculum included: clear learning objectives, multimodal instructional methods, and an IQIP. RESULTS: 2.63). In the first two cohorts of residents to complete the IQIPs, 67/72 (93%) completed at least one Plan-Do-Study-Act cycle, compared with 11/23 (48%) in the 2 years before the new curriculum. CONCLUSIONS: To ensure our trainees were attaining the educational goal of competence in QIPS, we introduced a revised QIPS curriculum and embedded an evaluation rooted in improvement science. This study adds to the limited literature which uses continuous quality improvement to enhance QIPS education, which is particularly needed in mental health.
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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 | 0.077 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".