Supporting relationships, providing presence: Arts-informed approaches to LTC staff dementia care literacy
Bibliographic record
Abstract
Abstract Early findings and insights are shared from an interpretive analysis of interviews with 15 leaders in arts-based approaches in dementia care. This was conducted as part of a larger project that has the goal of operationalizing ‘good’ literacy in social and relational care in long-term care (LTC). Interviews aimed to identify promising directions in arts-based approaches in education and training for LTC staff provincially, nationally, and internationally, and understand how they contribute to ‘good’ mental health and dementia literacy. Participants were recruited using a purposive snowball sampling method and semi-structured interview guide. Interviews were conducted in-person, via telephone and Zoom, and digitally recorded. Arts-making enhances mental health and dementia literacy of LTC staff by: 1) supporting relationships by generating trust and collaboration among persons living with mental health conditions and/or dementia, arts facilitators, and family members; 2) creating alternative communication spaces that allow people to see themselves and be seen by others from new perspectives; 3) fostering an artistic sensibility that: encourages imagination and empathy, brings spontaneity and playfulness to interactions, and disrupts restrictive expectations entrenched in typical caregiver/care receiver relationships. ‘Good’ literacy involves a relational and spatial awareness which manifests in the form of an artistic sensibility. Arts-based approaches can be used to enhance quality care by capacitating staff in the art of being open and curious, nimble and flexible, in how they know and make connections on an interpersonal level, in the moment.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.004 |
| 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".