IMPROVING THE ENGAGEMENT OF RESIDENTS WITH DEMENTIA IN ADVANCE CARE PLANNING DISCUSSIONS IN LONG-TERM CARE
Bibliographic record
Abstract
Abstract As part of the Strengthening a Palliative Approach in Long Term Care (SPA-LTC), we have designed and evaluated a number of tools and resources to promote a palliative approach in dementia, including a Canadian version of the Conversation Starter Kit (CSK) booklet. We used participatory action research to design tools for use with care providers based on survey data and interviews. Data was analyzed using thematic analysis and descriptive statistics. We found that residents reported that they engaged in asking more questions to their care provider after completing the CSK booklet. However, care providers stated that they did not feel comfortable addressing questions and concerns raised by residents and their family members. Hence, we co-designed two tools to help build capacity among care providers to support ACP discussions with residents and families, including an e-learning module about ACP discussions for people with dementia and a Conversation Guide. Pilot findings of these tools suggest that staff appreciate the information and tools to enable them to initiate ACP discussions and address difficult conversations. Clearly, residents with dementia and their families need to be supported to engage in ACP discussions early on in their disease trajectory so that residents are afforded the opportunity to voice their values and wishes for end of life care. At the same time, care providers need to have the skills (e.g., listening, probing) and comfort to engage in these discussions to support and help prepare residents and families for important decisions that need to be made later on.
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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 | 0.022 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".