From Today to Tomorrow: Leveraging Digital Health to Move toward Health for All
Bibliographic record
Abstract
This series of papers explores the concept of essential digital health for the underserved. Several cross-cutting themes are highlighted in this paper, for example: (1) harmonizing journeys of different patient groups to understand diverse perspectives; (2) engaging health professionals in interoperability, change management and health human resource capacity building; (3) ensuring harmonization of micro, meso and macro levels of health services delivery; and (4) integrating evaluation iteratively to enable continuous improvement and learning. Adopting a learning health system (LHS) approach facilitates iterative growth and evolution, incorporating concepts from the software industry, as well as participatory processes such as failing forward, developing ecosystems for collaboration and engagement of stakeholders. The example of HealthLink BC's 811 as a digital front door is used to demonstrate how an LHS approach can enable meaningful system change. We welcome further dialogues and discussion on existing and emerging examples of health system implementation approaches that can help our Canadian health systems move continuously and progressively closer toward the ultimate goal of Health for All (WHO 2023).
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.019 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.065 | 0.064 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".