Experiential Learning in Project-Based Quality Improvement Education: Questioning Assumptions and Identifying Future Directions
Bibliographic record
Abstract
PURPOSE: Project-based experiential learning is a defining element of quality improvement (QI) education despite ongoing challenges and uncertainties. The authors examined stakeholders' perceptions and experiences of QI project-based learning to increase understanding of factors that influence learning and project experiences. METHOD: The authors used a case study approach to examine QI project-based learning in 3 advanced longitudinal QI programs, 2 at the University of Toronto and 1 at an academic tertiary-care hospital. From March 2016 to June 2017, they undertook 135 hours of education program observation and 58 interviews with learners, program directors, project coaches, and institutional leaders and reviewed relevant documents. They analyzed data using a conventional and directed data analysis approach. RESULTS: The findings provide insight into 5 key factors that influenced participants' project-based learning experiences and outcomes: (1) variable emphasis on learning versus project objectives and resulting benefits, tensions, and consequences; (2) challenges integrating the QI project into the curriculum timeline; (3) project coaching factors (e.g., ability, capacity, role clarity); (4) participants' differing access to resources and ability to direct a QI project given their professional roles; and (5) workplace environment influence on project success. CONCLUSIONS: The findings contribute to an empirical basis toward more effective experiential learning in QI by identifying factors to target and optimize. Expanding conceptualizations of project-based learning for QI education beyond learner-initiated, time-bound projects, which are at the core of many QI educational initiatives, may be necessary to improve learning and project outcomes.
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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 | 0.116 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".