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 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 | 0.001 |
| 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".