‘If we can't do more, let's do it differently!': using appreciative inquiry to promote innovative ideas for better health care work environments
Bibliographic record
Abstract
AIM: To examine the use of appreciative inquiry to promote the emergence of innovative ideas regarding the reorganization of health care services. BACKGROUND: With persistent employee dissatisfaction with work environments, experts are calling for radical changes in health care organizations. Appreciative inquiry is a transformational change process based on the premise that nurses and health care workers are accumulators and producers of knowledge who are agents of change. METHODS: A multiple embedded case study was conducted in two interdisciplinary groups in outpatient cancer care to better understand the emergence and implementation of innovative ideas. RESULTS: The appreciative inquiry process and the diversity of the group promoted the emergence and adoption of innovative ideas. Nurses mostly proposed new ideas about work reorganization. Both groups adopted ideas related to interdisciplinary networks and collaboration. A forum was created to examine health care quality and efficiency issues in the delivery of cancer care. CONCLUSION: This study makes a contribution to the literature that examines micro systems change processes and how ideas evolve in an interdisciplinary context. IMPLICATIONS FOR NURSING MANAGEMENT: The appreciative inquiry process created an opportunity for team members to meet and share their successes while proposing innovative ideas about care delivery. Managers need to support the implementation of the proposed ideas to sustain the momentum engendered by the appreciative inquiry process.
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 | 0.027 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".