From fragmentation to functionality: Enhancing coherence of digital health integration in health systems
Bibliographic record
Abstract
Digital health programs continue to be implemented within Canadian health systems at a steady pace. The effectiveness of digital health initiatives has been rigorously analyzed, with both benefits and drawbacks extensively commented on. While the discussion about digital health continues, both positive and negative perspectives of it are approaching saturation in their themes. Accepting that digital health is here to stay post-pandemic, the focus should shift to strategies and supports needed to avoid the fragmentation of care through digital health implementation. This short article poses three questions which policy-makers and decision-makers should explore as part of a level-setting exercise with involved stakeholders at the outset of a digital health program's consideration. An implementation team should design the digital health program to have equity as its foundational focus, conduct value-based evaluations, and position the program in a learning health system framework to guard against the fragmentation of care.
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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 | 0.071 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.051 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.003 | 0.038 |
| Research integrity | 0.003 | 0.006 |
| 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".