A critical examination of the role of appreciative inquiry within an interprofessional education initiative
Bibliographic record
Abstract
Appreciative inquiry (AI) is a relatively new approach to initiating or managing organizational change that is associated with the 'positiveness' movement in psychology and its offshoot positive organizational scholarship. Rather than dwelling upon problems related to change, AI encourages individuals to adopt a positive, constructive approach to managing change. In recent years, AI has been used to initiate change across a broad range of public and private sector organizations. In this article, we report findings from a subset of 50 interviews gathered in a wider study of interprofessional education (IPE) in which AI was employed as a change agent for implementing IPE in a number of health care institutions in a North American setting. A multiple case study approach. (Yin, 2002) was employed in the wider study and semi-structured interviews were undertaken with participants both before their IPE programs and directly afterwards to obtain a detailed understanding of their expectations and experiences of IPE. Interviews were analyzed in an inductive thematic manner in order to produce key emergent themes from each of the IPE programs. A process of re-analysis provided a set of themes which offered an understanding of the role of AI within this IPE initiative. Our findings identify a strong resonance and fit for AI both among the health and social care professionals who participated in this initiative. Numerous individuals commented on the enthusiasm and energy AI engendered, while praising its ability to enhance their working lives and interprofessional relationships. Yet a number of difficulties were also reported. These focused on problems with the translation of the AI process into achievable structural level (e.g. professional, cultural) changes. Based on these findings, the article goes on to argue that the use of AI can overlook a number of structural factors, which will ultimately limit its ability to actually secure meaningful and lasting change within health care.
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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 | 0.063 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.032 | 0.054 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".