Bibliographic record
Abstract
Patients with dementia in acute care often experience poor outcomes, as nurses and other staff in hospitals are not equipped to provide good dementia care. Person-centred care has been recognized as the best practice for dementia care, but its application in hospital environments remains unclear. This action research involved patients with dementia, a team of staff members, and public advisors to co-create changes in a medical unit. The objectives of the project were to: (a) develop person-centred care in a medical unit, (b) explore ways to support the involvement of patients with dementia in research, (c) examine the processes of staff engagement for bringing together staff from different disciplines to co-inquire, and (d) evaluate the impact of research on the process of change and identify the lessons learnt to inform practice, education, policy, and research. Various methods were used such as: interviewing patients with dementia, focus group sessions with a team of inter-disciplinary staff, and participant observations. In this thesis, I argue for a new positive and collaborative approach that views change as a continuous process. In the past, the problem-focused model that sees change as fixing people has largely failed with regards to advancing practice developments in dementia care. An important outcome of this research is the heuristic guide ‘Team Engagement Action Making’ (TEAM), which can be used to support teams to engage staff in co-creating positive change. The results of this study indicate that appreciative inquiry is a useful strategy for engaging people on a team to learn together and to co-create a better future of care. The findings also suggest that more attention should be paid to the dynamic inter-connection of research and practice, rather than just one or the other. The results demonstrate that action research can affect the process of change by generating positive energy, attitude change, and momentum for action activities in the unit and beyond. Future research should further explore strategies that would maximize the potential of bringing patients, families, researchers, and practitioners to work together for positive change.
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 | 0.042 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".