Relationship-Centered Dementia Care: Insights from a Community-Based Culture Change Coalition
Bibliographic record
Abstract
This article reports on the work of a community-based culture change coalition affiliated with the Partnerships in Dementia Care Alliance, a research network committed to strengthening dementia care through supporting relationship-centered care approaches. Research to date emphasizes negative aspects of dementia care relationships. Drawing on data the culture change coalition collected as part of their culture change work using participatory action research guided by appreciative inquiry, this article examines what relationship qualities contributed to positive dementia care experiences and how positive relationships were created. Five types of care partners participated in the study through questionnaires, focus groups, and a mini appreciative inquiry summit. Data were analyzed collaboratively with culture change coalition members. Early in the analysis process, the aspiration statement "Relationships are at the heart of dementia care in [name of] County," was developed and informed further thematic analysis. Findings revealed several relationship characteristics including friendship, commonality of experience, developing trust and feeling appreciated, reciprocity, and taking time/making time for relationships. As this article illuminates, relationship-centered programs and policies have the potential to foster positive dementia care experiences among diverse care partners in community settings.
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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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.036 | 0.017 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".