Current Landscape of AgeTech Implementation and Adoption in Canadian Healthcare System: A scoping review
Bibliographic record
Abstract
This scoping review aims to identify AI/digital technology interventions adopted and implemented by the Canadian healthcare system for health care and health service delivery to older adults for various health conditions in different healthcare settings. It will also examine the facilitators and barriers to adoption and implementation from users' and the organizational system's perspective for the identified interventions and their outcomes for patients/clients and the healthcare system. The relevance of this scoping review is twofold. It is conducted concurrently with an implementation and evaluation study of an AI solution for wound care. It intends to support the research project titled "Implementation and Evaluation of Swift, a Digital Wound Care Solution: Implications for Adoption and Impact on the Quintuple Aim." The study uses implementation research methods to understand the adoption and implementation experiences and perspectives (patients, providers and decision-makers) and to assess the clinical outcomes of implementing the Swift Skin and Wound solution (Swift) within two local health systems in Ontario. Second, coupled with the findings (adoption and implementation experiences, facilitators and barriers to scale-up and sustainability) of the empirical study, this scoping review will guide the development of a health system implementation framework that can inform the implementation and adoption of AI-driven AgeTech, including Swift in the two identified systems and other local health systems within Canada. Note: We have included the entire protocol as an attachement, which includes the references cited in the sections.
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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 | 0.066 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.034 | 0.061 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".