Translating relational theories into dementia care using research-informed drama
Bibliographic record
Abstract
Culture change initiatives in older adult and dementia care have called for a new care paradigm informed by a relational ontology and relational theories. Yet translating these humanizing principles into practice has proven difficult; traditional approaches to knowledge translation have not had a sustained impact. One of the most significant impediments to transforming the culture of care has been the lack of understanding of what it means to be and act relational in practice. A growing body of research is demonstrating the potential of critical arts-based inquiry/pedagogy (CABI/P) for translating theory in accessible and relevant ways for diverse audiences and for shifting images, understandings and actions in healthcare in more effective ways. As part of a larger longitudinal project, this paper explores the immediate impacts of a research-informed drama called ‘Cracked: new light on dementia,’ and how it might enhance understandings of relational caring for staff working in long-term care (LTC) homes. Data from post-performance focus group/interview discussions with staff working in two different LTC settings point to the effectiveness of Cracked in translating key principles of relational theories into older adult and dementia care and the possibilities of CABI/P in gerontological education more broadly. Although organizational and broader system change is needed to achieve sustained impact of relational caring practices, CABI/P, such as Cracked, has the potential to expose and challenge entrenched assumptions, policies, practices, and to imagine and effect more compassionate, humane, and equitable care.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".