A digital health platform to manage COVID-19: decentralizing technology to empower rural and remote jurisdictions
Bibliographic record
Abstract
INTRODUCTION: The variation of coronavirus disease (COVID-19) outbreaks across rural and remote jurisdictions makes it imperative to invest in scalable digital health platforms to not only minimize the impact of subsequent COVID-19 outbreaks, but also to utilize such approaches to predict and prevent future communicable and non-communicable diseases. METHODS: The methodology of the digital health platform comprised: (1) Ethical Real-Time Surveillance to Monitor Risk: evidence-based artificial intelligence-driven individual and community risk assessment of COVID-19 by engaging citizens using their own smartphones; (2) Citizen Empowerment and Data Ownership: active engagement of citizens using smartphone application (app) features, while enabling data ownership; and (3) Privacy: development of algorithms that store sensitive data directly on mobile devices. RESULTS: The result is a community-engaged, innovative, and scalable digital health platform, with three key features: (1) Prevention: this feature is based on risky and healthy behaviours, and has the sophistication to continuously engage citizens; (2) Public Health Communication: based on their risk profile and behaviour, citizens receive specific public health communication that helps them make informed decisions; and (3) Precision Medicine: risk assessment and behaviour modification is individualized so that the frequency, type, and intensity of engagement is based on individual risk profile. DISCUSSION: This digital health platform enables the decentralization of digital technology to effect systems-level changes. With more than 6 billion smartphone subscriptions globally, digital health platforms enable direct engagement with large populations in near real-time to monitor, mitigate, and manage public health crises, particularly in rural communities that do not have equitable access to health services.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".