Family physician and specialist care for persons with dementia living in the community: through thick and thin
Bibliographic record
Abstract
BACKGROUND: Persons with dementia living in the community are vulnerable to service disruptions as they rely on a mix of outpatient care from different types of physicians. To demonstrate how outpatient physician visits evolved among persons with dementia during a health crisis compared to the prior year. METHODS: Using administrative databases, two retrospective cohorts (2019/pre-COVID-19 pandemic; 2020/pandemic) of community-dwelling persons with dementia aged 65+ were identified within three Canadian provinces (Alberta, Ontario, and Quebec). We measured the rates of visits (total/virtual/in-person) to family physicians, cognitive specialists (neurologists, geriatricians, and psychiatrists), and other specialists. Provincial incident rate ratios (IRR) and 95% confidence intervals (CIs) were calculated by comparing three pandemic periods (first wave; interim period; second wave) to the corresponding pre-pandemic periods (reference) and subsequently pooled using a meta-analysis to obtain overall estimates. RESULTS: Pre-pandemic (n = 160 288) and pandemic (n = 166 392) cohorts had similar characteristics. Although significant increases in family physician visits within provinces were observed during certain periods, there was no significant change in overall estimates compared to pre-pandemic levels. Overall cognitive (IRR 0.85, CI 0.80-0.90) and other specialist (IRR 0.71, 0.56-0.90) visits were significantly lower in the first wave compared to pre-pandemic period. There was a significant increase in virtual visits and a significant decline in in-person visits across all physician types throughout the pandemic periods. CONCLUSION: Family physicians are the cornerstone of sustaining dementia care during health crises such as the COVID-19 pandemic, in part due to virtual care. Future research may investigate long-term outcomes of abrupt disruption in specialist and other community care.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".