Effect of the Organisational Development Tool Appreciative Inquiry [Internet]
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
The Norwegian Knowledge Centre for the Health Services was commissioned by The Regional Health Authority of South-Eastern Norway, Unit for Service Development and Cooperation to summarize available research on the effect of the organisational change methodology Appreciative Inquiry (AI). The aim of this review is to answer whether AI was more effective than other organizational development methods during a process of change in an organization. Even though we wished to focus on changes in the health services, we did not restrict the outcomes, where the intervention had taken place or what kind of organisational change that was studied.We searched for controlled studies of effect both in medical and social electronic databases and identified 367 references. We included the six studies that had a control group. All were controlled before and after studies.The included studies were conducted in different enterprises, a ward in a hospital in England, US Postal Services, a chain of fast food restaurants, a manufacturer of freight elevator doors, a trucking company, all in the USA, and a group of students in Canada. Several of the studies had more than one outcome, but none had measured an outcome in the same way. The outcomes comprised absence due to sickness, turnover, attitudes toward colleagues that make mistakes, conflict management, task quality, trust in the recourses of the group and a wish for future cooperation.We assessed the studies to have an unclear or high risk of bias. The quality of documentation for effect of AI was very low, and we cannot draw clear conclusions. Some of the included studies reported that AI seemed to be more efficient than other organisational development tools, others not. Some of the included studies also reported that AI sometimes was not more efficient than not using any development tools. Future studies on the effect of AI should be larger and of better quality than the identified studies. The elements of AI that is the focus of research should be clearly specified and the outcomes more precisely defined.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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