Experiences of care partners and residents with the Long-Term Care Palliative Toolkit during the COVID-19 pandemic: A multiple methods study
Bibliographic record
Abstract
Background: With a large burden of suffering and death in 2020 due to COVID-19 in long-term care (LTC) homes resulting in restrictions of visitations, there is a need for a formal virtual intervention to support families/friends (i.e., care partners) and residents around palliative care, including planning for end-of-life when outbreaks like these occur. The LTC Palliative Toolkit includes informational resources for care partners, residents, and healthcare providers about the trajectory of life-limiting chronic illnesses (i.e., frailty, dementia, heart failure, kidney disease, lung disease) and Palliative Care Conferences (PCCs). Objective: To evaluate the impact of the LTC Palliative Toolkit on preparedness for end-of-life and satisfaction with information and to explore the experiences of care partners and residents with the virtual delivery of the components of the LTC Palliative Toolkit (i.e., informational pamphlets and PCCs). Methods: A multiple methods design was employed. Three LTC homes, one from each province (Ontario, New Brunswick, and Saskatchewan, Canada), were selected to reflect diverse contexts (e.g., ownership, staff turnover, facility size, and location). Caring Ahead surveys focusing on actions, dementia knowledge, communication, and emotions and support needs were conducted with care partners before and after PCCs to evaluate how prepared they felt about their relative or friend's end-of-life and their satisfaction. Some care partners and residents completed telephone semi-structured interviews to explore their experiences with care received. Results: = 0.016). Qualitative interviews identified that the LTC Palliative Toolkit was a valuable intervention for both care partners and residents. Conclusion: The LTC Palliative Toolkit is suitable for use in any context and demonstrated high acceptability during the COVID-19 pandemic.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| 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".