“It is like stepping into another world”: Exploring the possibilities of using appreciative participatory action research to guide culture change work in community and long-term care
Bibliographic record
Abstract
This paper highlights the possibilities for transformation that exist when a diverse group of participants interested in working together to change the culture of dementia care in long-term care and community care settings use appreciative participatory action research to guide their culture change efforts. These transformations happened throughout the culture change process using appreciative participatory action research. For instance, using appreciative participatory action research to guide the culture change process provided participants with the opportunity to build stronger professional and personal relationships in their respective care communities. Culture change transformations also stemmed from the appreciative participatory action research process, as participants recognized the importance of finding ways to include persons with dementia/residents in the process and they developed an appreciation for the valuable contributions persons with dementia/residents can make to culture change work. These culture chance possibilities demonstrate the value in using appreciative participatory action research to guide culture change in long-term care and community care contexts. These possibilities also illustrate the importance of paying closer attention to the culture change process itself, rather than solely the outcomes of the process, given that the possibilities for transformation that can take place throughout the process can help to build momentum, propelling culture change efforts forward in healthcare contexts.
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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 | 0.218 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.032 | 0.082 |
| Scholarly communication | 0.029 | 0.027 |
| Open science | 0.007 | 0.037 |
| Research integrity | 0.008 | 0.012 |
| 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".