Virtual care use in Canada: Variation across sociodemographic and health-related factors
Bibliographic record
Abstract
Background: The COVID-19 pandemic changed how Canadians accessed health care, increasing their use of virtual services. While virtual care use decreased after the pandemic lockdowns, it continues to play an important role in health care delivery. More information is needed about variations in virtual care use by sociodemographic and health characteristics. Data and methods: Data from the 2023 Canadian Social Survey - Quality of Life, Virtual Health Care and Trust were used. Descriptive statistics estimated the types of health care appointments individuals had in the past 12 months, access to virtual care, the types of health care providers consulted virtually, and the reasons individuals declined virtual appointments. Multivariate analyses examined whether sociodemographic and health characteristics were associated with patients' virtual care use. Results: Over half of patients (57.5%) had in-person appointments only, 5.3% had virtual appointments only, and over one-third (37.2%) had both types of appointments. Of individuals who sought or were offered virtual care, 78.5% had a virtual appointment. Most virtual care users consulted a family doctor, general practitioner, or nurse practitioner only (62.1%). Higher education, not having a regular health care provider, and multimorbidity were positively associated with virtual care use. Greater comfort with in-person appointments was the most common reason for declining virtual care. Interpretation: While many individuals in Canada accessed virtual care, only a small proportion had virtual appointments only. Virtual care use varied by some sociodemographic and health factors, such as education and multimorbidity. Technological barriers were not a common reason for declining virtual appointments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".