SUPPORTING RESIDENTS WITH DEMENTIA LIVING AND DYING IN LONG-TERM CARE AND THEIR FAMILIES DURING COVID-19
Bibliographic record
Abstract
Abstract TThis presentation will share research findings about experiences during COVID-19 about implementing a virtual palliative toolkit in long-term care in Canada. The toolkit includes tools and practices to: (a) engage residents and families with dementia within a palliative approach to care, (b) develop workforce capacity through online education modules, (c) reduce stress and improve psychological health of residents, families, and staff, and (d) develop organizational structures and processes to promote a palliative approach to care. Individual interviews were conducted with residents, family members, and staff before implementing a palliative toolkit and after using it. Findings highlighted the negative impacts of COVID-19 on resident health due to isolation within home, preventing family from being at the bedside and cancelling stimulatory activities especially at end of life that were exacerbated by the lack of resources and government supports. Families appreciated the virtual supports and stated that they helped prepare them for their loved ones’ death while feeling more empowered, engaged, and supported in their journey. Although feedback from families was mostly positive, stating the virtual toolkit improved accessibility to information and supports, it was clear that some misunderstood terms, particularly what a palliative approach to care means; and others had challenges navigating the virtual platform to use the toolkit. Future work is needed to make the virtual tools more user-friendly so that they can be scaled up more widely.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".