APPRECIATIVE INQUIRY: BRIDGING RESEARCH AND PRACTICE IN A HOSPITAL SETTING
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
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.
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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