Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research examines the gaps in adopting and accepting artificial intelligence (AI) in eHealth systems and proposes potential strategies for successful implementation. This paper begins by providing an overview of AI in eHealth systems in Canada and outlines the systematic methodology employed in this review. Subsequently, a theory-driven research agenda is presented, followed by the concluding observations. To address prior research gaps and identify promising areas for integration, this study reviews the existing literature on AI in eHealth in Canada. As a new perspective and meaningful advancement, the current findings offer novel insights and groundbreaking research for the future of Canadian eHealth systems based on AI. Strategies, such as capacity-building partnerships (between countries with similar best practices and Canada) and cultural/ethical regulation improvements, can pave the way for AI's transformative role in improving e-healthcare outcomes, aligning with the United Nations' Sustainable Development Goals.
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it