An Appreciative Inquiry Approach to Practice Improvement and Transformative Change in Health Care Settings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it