2.F. Workshop: Innovations for chronic diseases management: complementing usual care with digital therapeutics
Bibliographic record
Abstract
Abstract The management of chronic non-communicable diseases (NCDs), including both physical and mental disorders, remains a pressing global health challenge in high-income countries and low-resource settings. Digital health technology, also known as eHealth, could help improve the management of chronic diseases, in the post-pandemic era, by facilitating data exchange between patients and health professionals. Specifically, digital therapeutics, which utilize technology to deliver evidence-based interventions, have demonstrated potential to complement traditional care approaches and improve outcomes for patients with chronic NCDs. The proposed joint workshop, organized by two EPH sections (chronic disease and digital health and communication), will provide four presentations on innovative approaches to improve the management of major chronic disease, with focus on digital health technologies, which can provide alternative cost-effective options in both high-income countries and low-resource settings. The workshop will explore how digital therapeutics can enhance the management of chronic NCDs, such as diabetes and common mental disorders among others, by complementing existing healthcare practices. The selected speakers are international leaders in the field and will represent different geographic contexts within Europe (Malta, Italy, and Switzerland) and North America (Canada). Key messages • Digital health technologies can provide alternative cost-effective options in chronic disease management. • Digital therapeutics can enhance the management of chronic NCDs by complementing existing healthcare practices.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.040 | 0.012 |
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".