Access to Virtual Physician Care among Persons with Dementia in Urban and Rural Areas: A Repeated Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, virtual physician visits rapidly increased among community-dwelling older persons living with dementia (PLWD) in Ontario. Rural residents often have less access to medical care compared to their urban counterparts, and it is unclear whether access to virtual care was equitable between PLWD in urban versus rural locations. METHODS: Using population-based health administrative data and a repeated cross-sectional study design, we identified and described community-dwelling PLWD between March 2020 and August 2022 in Ontario, Canada. Poisson regression was used to calculate rate ratios (RR) and 95% confidence intervals comparing rates of virtual visits between rural and urban PLWD by key physician specialties: family physicians, neurologists and psychiatrists/geriatricians. RESULTS: = 11,304) resided in rural areas. Rural PLWD were slightly younger compared to their urban counterparts (mean age = 81 vs. 82 years; standardized difference = 0.16). There were no differences across areas by sex or income quintile. In adjusted models, rates of virtual visits were significantly lower for rural compared to urban PLWD across all specialties: family physicians (RR = 0.71 [0.69-0.73]), neurologists (RR = 0.79 [0.75-0.83]) and psychiatrists/geriatricians (RR = 0.72 [0.68-0.76]). CONCLUSIONS: PLWD in rural areas had significantly lower rates of virtual family physician, neurologist and psychiatrist/geriatrician visits compared to urban dwellers during the study period. This finding raises important issues regarding access to primary and specialist healthcare services for rural PLWD. Future work should explore barriers to care to improve health care access among PLWD in rural communities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".