The Experience of Caregivers of Older Adults With Dementia in Using Telemedicine in a Primary Care Setting of Canada During COVID-19
Bibliographic record
Abstract
Background: Primary care is essential in dementia management, offering diagnosis, treatment, and support for people living with dementia (PLWD) and their caregivers. Telemedicine became a key advancement during the COVID-19 pandemic, offering crucial access to care. This study explores the pros and cons of telemedicine for dementia care during the pandemic to guide future improvements. Methods: Data collection involved semi-structured interviews with caregivers recruited from a Montreal memory clinic and secondary analysis of two other studies related to dementia and telemedicine, focusing on the educational needs of patients and the impact of the pandemic on health-care services. Data analysis employed the framework method, combining inductive and deductive approaches to code the data and develop categories aligned with Chang's framework, providing insights into caregivers' experiences and the challenges and benefits of telemedicine. Results: The study involved interviews with four caregivers of people with dementia, complemented by secondary analysis from two Canadian studies. Through framework analysis, four themes were developed: relationship and communication; the advantages and selective suitability of telemedicine (TM) in dementia care; preferences for in-person consultations; and the need to improve awareness and technical confidence in TM. Conclusion: This study highlights the potential of telemedicine (TM) as an effective modality for dementia care, particularly during situations like the COVID-19 pandemic, but emphasizes that it cannot fully replace in-person consultations due to the enduring preference for face-to-face interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".