Appreciative inquiry in health care
Bibliographic record
Abstract
Organisational change in health care is a complex, non-linear process that must evolve in response to shifts in social, economic and political environments. Given this constant flux, current organisational models fail to address the needs of the patients and healthcare workers, who are becoming increasingly dissatisfied. The radical changes required must influence work design and workforce management, while focusing on the interactions between actors within the systems. This article presents an approach to organisational change: appreciative inquiry. By building on positive ideas and images emerging from individuals or groups, this approach fosters learning andpromotes the emergence of innovative ideas. A review of the literature from 1990 to 2009 was undertaken to describe the application of appreciative inquiry in health care. After a brief description of the theoretical foundations and the process of appreciative inquiry, the studies and projects uncovered in the review are presented. The limits and advantages of using appreciative inquiry to promote organisational change in health care are discussed.
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.
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 | 0.051 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.101 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.009 | 0.010 |
| 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".