An Appreciative Inquiry Approach to Practice Improvement and Transformative Change in Health Care Settings
Bibliographic record
Abstract
Amid tremendous changes and widespread dissatisfaction with the current health care system, many approaches to improve practice have emerged; however, their effects on quality of care have been disappointing. This article describes the application of a new approach to promote organizational improvement and transformation that is built upon collective goals and personal motivations, invites participation at all levels of the organization and connected community, and taps into latent creativity and energy. The essential elements of the appreciative inquiry (AI) process include identification of an appreciative topic and acting on this theme through 4 steps: Discovery, Dream, Design, and Destiny. We describe each step in detail and provide a case study example, drawn from a composite of practices, to highlight opportunities and challenges that may be encountered in applying AI. AI is a unique process that offers practice members an opportunity to reflect on the existing strengths within the practice, leads them to discover what is important, and builds a collective vision of the preferred future. New approaches such as AI have the potential to transform practices, improve patient care, and enhance individual and group motivation by changing the way participants think about, approach, and envision the future.
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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 | 0.071 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.016 | 0.081 |
| Scholarly communication | 0.025 | 0.021 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.007 | 0.011 |
| 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".