Implementing Home-Based Digital Health in Rural Canada: A Scoping Review
Bibliographic record
Abstract
Objective: This scoping review maps the current evidence on implementing home-based digital health technologies in rural Canada. It examines available readiness tools and indicators, stakeholder perspectives, barriers, and outcomes to provide evidence-based insights for successful implementation. Methods: A comprehensive search was conducted in Ovid MEDLINE, IEEE Xplore, and Scopus between February and March 2025. Eligible studies focused on patient-facing, home-based digital health technologies in rural or remote Canadian contexts. Articles addressing pre-implementation, implementation, or adoption of home-based digital health solutions were also included. Data extraction and thematic analysis were performed to synthesize findings. Results: Sixteen studies met the inclusion criteria, spanning diverse rural regions of British Columbia, Ontario, and several Prairie and Atlantic provinces. Findings were categorized under four major themes: (1) readiness tools, frameworks, and indicators; (2) patient and provider perspectives; (3) barriers and corresponding strategies; and (4) outcomes and impacts of home-based digital health implementation in rural Canada. While patients and providers are generally positive towards home-based digital health technologies, several context-dependent factors influence their success. Key barriers include digital divides, infrastructure limitations, and varying digital literacy. Effective implementation necessitates addressing these challenges through tailored strategies, such as culturally sensitive design, infrastructure development, digital literacy training, and community engagement. Conclusion: Home-based digital health technologies have the potential to improve healthcare access and outcomes in rural Canada. Successful implementation requires careful consideration of contextual factors, proactive barrier mitigation, and a focus on co-design with users to ensure equitable access and outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".