Factors associated with access to virtual care in older adults: A cross-sectional study
Bibliographic record
Abstract
Abstract Background During the COVID-19 pandemic, virtual care (i.e. telephone or videoconference) has played a critical role. However, concerns were raised regarding equitable access for older adults, in particular, given potential advantages of videoconference-as opposed to telephone-based assessment. Our objective was to describe patient-specific factors associated with different modes of virtual healthcare. Methods We reviewed medical records of all patients assessed virtually in the geriatric medicine clinic at St. Michael’s Hospital, Toronto, Canada, between March 17 and July 13, 2020. We derived adjusted odds ratios (OR), risk differences (RD), and marginal and predicted probabilities, with 95% confidence intervals, from a multivariable logistic regression model, which tested the association between having a videoconference assessment (vs. a telephone assessment) and patient age, sex, ability to use a computer, education, frailty (measured on the Clinical Frailty Scale), history of cognitive impairment, and immigration history; language of assessment, and caregiver involvement in assessment. Results Our study included 330 patients (227 telephone and 103 videoconference assessments). Frailty (adjusted OR 0.62, 0.45 to 0.85; adjusted RD -0.08, -0.09 to -0.06) and absence of a caregiver (adjusted OR 0.12, 0.06 to 0.24; adjusted RD -0.35, -0.43 to -0.26) were associated with lower odds of videoconference assessment. For example, an 80-year-old woman with mild frailty who immigrated to Canada, speaks English, attained a post-secondary education, does not have cognitive impairment, and uses a computer had a 60% (39% to 80%) predicted probability of videoconference assessment if a caregiver was present compared to 15% (3% to 26%) without a caregiver. Only 32 of 98 (32.7%) patients who could independently use a computer participated in videoconference assessments. Conclusion Given the recent expansion of virtual care, we must urgently implement and evaluate strategies that optimise equitable access to videoconference-based virtual care for older adults.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".