What faculty need to learn about improvement and how to teach it to others
Bibliographic record
Abstract
Quality improvement in health care, appropriately understood and applied, is one way to develop a sense of control over daily work. How can faculty members learn improvement methods, apply them to work and teach them to future health professionals? The paper outlines an improvement 'theory' and illustrates some ways it has been taught and learned by 10 interdisciplinary groups of faculty and students over the past 6 years. Eight domains constitute the content of improvement knowledge. They include: (1) health care as a system; (2) variation and measurement; (3) knowledge of the beneficiaries of health care services; (4) leading, following and making changes; (5) collaboration; (6) social context and accountability; (7) developing new locally useful knowledge; and (8) professional subject matter knowledge. Many lessons have been learned by 10 Local Interdisciplinary teams who have collaborated over the past 6 years including: (1) Systems knowledge is more effectively learned in the context of real work than in the classroom; (2) outcomes of care are beneficial sources of information to learn about the beneficiaries of care; and (3) the experience of collaboration with others-experts, colleagues, students and others-can be a learning tool in itself, especially in an inter-professional team. Involvement of students from multiple disciplines can enhance the impact of efforts to allocate resources in their organizations to building knowledge for improvement.
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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 | 0.037 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.022 | 0.034 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".