How Theory Can Inform Our Understanding of Experiential Learning in Quality Improvement Education
Bibliographic record
Abstract
It is widely accepted that quality improvement (QI) education should be experiential. Many training programs believe that making QI learning "hands-on" through project-based learning will translate into successful learning about QI. However, this pervasive and overly simplistic interpretation of experiential QI learning, and the general lack of empirical exploration of the factors that influence experiential learning processes, may limit the overall impact of project-based learning on educational outcomes.In this Perspective, the authors explore the opportunities afforded by a theoretically informed approach, to deepen understanding of the diverse factors that affect experiential QI learning processes in the clinical learning environment. The authors introduce the theoretical underpinnings of experiential learning more generally, and then draw on their experiences and data, obtained in organizing and studying QI education activities, to illuminate how sociocultural theories such as Billett's workplace learning theory, and sociomaterial perspectives such as actor-network theory, can provide valuable lenses for increasing our understanding of the varied individuals, objects, contexts, and their relationships that influence project-based experiential learning. The two theoretically informed approaches that the authors describe are amongst numerous others that can inform a QI education research agenda aimed at optimizing educational processes and outcomes. The authors conclude by highlighting how a theoretically informed QI education research agenda can advance the field of QI education; they then describe strategies for achieving this goal.
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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 | 0.025 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.063 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".