Digital Health Evaluation of Physically Disabled Canadians Compared to Those Without Chronic Conditions
Bibliographic record
Abstract
BACKGROUND: Nearly 8.0 million Canadians have one or more disabilities, and this population uses a disproportionate portion of healthcare resources. Digital health, including virtual care and health information interoperability, offers specific benefits but also specific challenges to patients with physical disabilities (PwPD). OBJECTIVE: This study examined the utilization of virtual care and digital health access among PwPD compared to those without chronic conditions (NCC) by examining the usage rates, preferred modalities, the impact of interoperability, and the role of access to personal digital health information. METHODS: A cross-sectional survey, termed the 2022 Canadian Digital Health Survey (CDHS), was conducted to target any Canadians over 16 who have used healthcare in Canada over the last 12 months. RESULTS: This study compared the PwPD group (n=674) with the NCC group (n=5273). PwPD were significantly older and had higher digital health service utilization among PwPD compared to NCC individuals. They reported higher engagement in digital health services like prescription management, telephone consultations, and remote patient monitoring. Healthcare system coordination challenges were more prevalent with 24.4% experiencing care delays and 31.8% reporting frustration. PwPD reported positive outcomes in accessing personal health information (PHI), setting health goals, avoiding doctor visits, and preventing ER visits. CONCLUSION: This study highlights the link between digital health quality and care outcomes in Canadian healthcare. It reveals a preference for telephone consultations in virtual care, emphasizing the importance of accessible technology. Future studies need to be completed to better understand people with PwPD and the larger group of patients with chronic conditions.
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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 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".