Virtual primary care for people living with dementia in Canada: Cross-sectional surveys of patients, care partners, and family physicians
Bibliographic record
Abstract
Abstract Background: Despite the importance of virtual primary care, the evidence informing optimal and sustainable provision of virtual care (VC) for people living with dementia (PLWD) and their care partners is scarce. We aimed to describe VC provided by family physicians (FPs) for PLWD in Canada. Methods: Concurrent mixed-methods design. We analyzed questions related to VC in three nationwide cross-sectional surveys conducted with PLWD, care partners, and FPs in the first year of the COVID-19 pandemic. Virtual care was defined as two-way synchronous communication by telephone and/or a web camera. The prevalence of VC use among FPs, PLWD, and care partners was described, and logistic regression models were used to determine factors (sociodemographic, urbanicity, frequency of and availability of support for connecting to FPs, and FPs’ practice characteristics) associated with VC use. Inductive thematic analysis was performed on responses to open-ended questions to explore FPs’ perceptions of barriers and facilitators to using VC. Results: 131 PLWD, 341 care partners, and 125 FPs participated. 61.2% of PLWD, 59.5% of care partners, and 77.4% of FPs had used VC. The models for PLWD (included age and ethnicity) and care partners (included gender, urbanicity, and receiving support from a family member/friend to connect with FP) were inconclusive. Among FPs, having more than 20 years of practice was significantly associated with a decreased likelihood of providing VC (OR=0.23, 95%CI: 0.08-0.62, p<0.01). Care preferences (decision stage), office/family support (preparation stage), technology and family presence (execution stage), and remuneration for FPs (compensation stage) were the most commonly recurring themes affecting VC use. Conclusions: Virtual primary dementia care uptake was substantial and mainly performed via telephone. From the perspective of FPs, optimal VC provision requires patient-care partner-physician shared decision-making, interoperability in healthcare, support for performing VC, and appropriate compensation. Virtual care facilitates access to primary care and minimizes potential disruptions to in-person care for PLWD; however, its outcomes need further investigation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".