Supporting people living with dementia and care partners throughout the COVID‐19 pandemic: Health service directions from the first wave in Calgary, Alberta
Bibliographic record
Abstract
Abstract Background The emergence of COVID‐19 (SARS‐CoV‐2) as a novel coronavirus in late 2019 necessitated public health measures that have impacted the provision of care for people living with dementia and their families. The rapid shift to virtual care across health and social care sectors meant that providers did not have the opportunity to benefit from an evidence‐based understanding about how and which services can safely and effectively be delivered virtually prior to public health measures being implemented. Additionally, isolation resulting from social distancing may be harming well‐being for families as formal and informal supports become less accessible. Method To understand lived experiences and necessary changes in models of care delivery for people living with dementia during the COVID‐19 pandemic in Canada, we remotely interviewed 20 dyads of people living with dementia and their care partners who normally attend a dementia specialty clinic in Calgary, Alberta, during a period where essential businesses were closed and health care had abruptly transitioned to telemedicine. Participants were 50% female and a majority of clinic patients in the dyad had a diagnosis of Alzheimer’s Disease (75%). A reflexive thematic analysis was used to analyze the interview and field note data. Result Themes regarding virtual service provision emerged through the iterative qualitative data analysis: (1) continuation of community‐based services for care partners and families delivered in innovative ways to meet support needs during the pandemic; (2) guidance in adapting to technology to enable accessible and effective treatment in a virtual care environment; (3) adapting the process and structure of virtual appointments to operationalize critical information provision while maintaining dignity for the person living with dementia. Conclusion The rapid move to virtual healthcare has influenced how and when people access health services. Health system innovation in the way we structure service models and care provision can mitigate barriers to maintaining high quality virtual health care for people living with dementia. In‐depth understandings of how health systems can provide high‐quality care in new virtual settings is key to maintaining quality of life for community‐dwelling people living with dementia and care partners in times of public health emergencies.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".