The Impact of a Global Pandemic on People Living with Dementia and Their Care Partners: Analysis of 417 Lived Experience Reports
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic is impacting the physical and emotional health of older adults living with dementia and their care partners. OBJECTIVE: Using a patient-centered approach, we explored the experiences and needs of people living with dementia and their care partners during the COVID-19 pandemic as part of an ongoing evaluation of dementia support services in British Columbia, Canada. METHODS: A survey instrument was developed around the priorities identified in the context of the COVID-19 and Dementia Task Force convened by the Alzheimer Society of Canada. RESULTS: A total of 417 surveys were analyzed. Overall, respondents were able to access information that was helpful for maintaining their own health and managing a period of social distancing. Care partners reported a number of serious concerns, including the inability to visit the person that they care for in long-term or palliative care. Participants also reported that the pandemic increased their levels of stress overall and that they felt lonelier and more isolated than they did before the pandemic. The use of technology was reported as a way to connect socially with their loved ones, with the majority of participants connecting with others at least twice per week. CONCLUSION: Looking at the complex effects of a global pandemic through the experiences of people living with dementia and their care partners is vital to inform healthcare priorities to restore their quality of life and health and better prepare for the future.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".