APPRECIATIVE INQUIRY: BRIDGING RESEARCH AND PRACTICE IN A HOSPITAL SETTING
Bibliographic record
Abstract
In this action study, researchers worked with a team of interdisciplinary practitioners to co-develop knowledge and practice in a medical unit of a large urban hospital in Canada. An appreciative inquiry approach was utilized to guide the project. This paper specifically focuses on examining the research experiences of practitioners, their accounts on how the research influenced their practice development to enact person-centred care. The project took place in the hospital’s medical unit. A total of 50 staff participants attended focus groups, including nursing staff, allied health practitioners, unit leaders, and physicians. One senior hospital administrator was interviewed individually. In total, 36 focus groups were conducted, to bring participants together to co-vision and co-develop person-centred care. Analysis of the data produced three themes: (a) appreciating the power of co-inquiry, (b) building team capacity, and (c) continuous development. Furthermore, ten key enablers for engaging staff in the research process were developed from the data. A conceptual tool, ‘Team Engagement Action Making’ (TEAM) has been created to support others to do similar work in practice development. A very practical use of the tool is for team discussion, as talking points to stimulate reflection on what needs to be considered to facilitate change. The study results demonstrated the appreciative inquiry approach has the potential to address gaps in knowledge by revealing ways to take action. Future research should further investigate how the appreciative inquiry approach may be used to support bridging research and practice.
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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 | 0.119 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.024 | 0.048 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.006 | 0.035 |
| Research integrity | 0.005 | 0.007 |
| 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".