Factors associated with virtual care access in older adults: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: virtual care has been critical during the COVID-19 pandemic, but there may be inequities in accessing different virtual modalities (i.e. telephone or videoconference). OBJECTIVE: to describe patient-specific factors associated with receiving different virtual care modalities. DESIGN: cross-sectional study. SETTING AND SUBJECTS: we reviewed medical records of all patients assessed virtually in the geriatric medicine clinic at St. Michael's Hospital, Toronto, Canada, between 17 March and 13 July 2020. METHODS: we derived adjusted odds ratios (OR), risk differences (RDs) 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. telephone) and patient age, sex, computer ability, education, frailty (Clinical Frailty Scale score), 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). The median population age was 83 (Q1-Q3, 76-88) and 45.2% were male. Frailty (adjusted OR 0.62, 0.45-0.85; adjusted RD -0.08, -0.09 to -0.06) and absence of a caregiver (adjusted OR 0.12, 0.06-0.24; adjusted RD -0.35, -0.43 to -0.26) were associated with lower odds of videoconference assessment. Only 32 of 98 (32.7%) patients who independently use a computer participated in videoconference assessments. CONCLUSIONS: older adults who are frail or lack a caregiver to attend assessments with them may not have equitable access to videoconference-based virtual care. Future research should evaluate interventions that support older adults in accessing videoconference assessments.
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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 | 0.000 | 0.000 |
| 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".