A realist synthesis of quality improvement curricula in undergraduate and postgraduate medical education: what works, for whom, and in what contexts?
Bibliographic record
Abstract
BACKGROUND: With the integration of quality improvement (QI) into competency-based models of physician training, there is an increasing requirement for medical students and residents to demonstrate competence in QI. There may be factors that commonly facilitate or inhibit the desired outcomes of QI curricula in undergraduate and postgraduate medical education. The purpose of this review was to synthesise attributes of QI curricula in undergraduate and postgraduate medical education associated with curricular outcomes. METHODS: A realist synthesis of peer-reviewed and grey literature was conducted to identify the common contexts, mechanisms, and outcomes of QI curricula in undergraduate and postgraduate medical education in order to develop a programme theory to articulate what works, for whom, and in what contexts. RESULTS: 18854 records underwent title and abstract screening, full texts of 609 records were appraised for eligibility, data were extracted from 358 studies, and 218 studies were included in the development and refinement of the final programme theory. Contexts included curricular strategies, levels of training, clinical settings, and organisational culture. Mechanisms were identified within the overall QI curricula itself (eg, clear expectations and deliverables, and protected time), in the didactic components (ie, content delivery strategies), and within the experiential components (eg, topic selection strategies, working with others, and mentorship). Mechanisms were often associated with certain contexts to promote educational and clinical outcomes. CONCLUSION: This research describes the various pedagogical strategies for teaching QI to medical learners and highlights the contexts and mechanisms that could potentially account for differences in educational and clinical outcomes of QI curricula. Educators may benefit from considering these contexts and mechanisms in the design and implementation of QI curricula to optimise the outcomes of training in this competency area.
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 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 | 0.060 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.032 | 0.024 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".