Integrated Care’s New Protagonist: The Expanding Role of Digital Health
Bibliographic record
Abstract
Digital health technologies hold significant promise to advance both functional and normative health and social care integration. The COVID-19 pandemic has created a window of opportunity to rapidly advance the adoption of digital solutions which can improve activities that support integration at clinical, professional, organizational and system levels. Global examples demonstrate how the pandemic has also created opportunities to use technology to address core values of integrated care like person-centredness and coordination. However, rapid and reactive changes could lead to increased fragmentation and exacerbate health inequity. This perspective paper outlines some of the opportunities and threats to advancing integrated care presented by the rapid adoption of digital health tools, suggesting we maintain a long view to ensure the stage we set today will mean greater integration tomorrow.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.014 | 0.028 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".