COVID-19’s Impact on Digital Health Adoption: The Growing Gap Between a Technological and a Cultural Transformation
Bibliographic record
Abstract
Health care in the 21st century has started undergoing major changes due to the rising number of patients with chronic conditions; increased access to new technologies, medical information, and peer support via the internet; and the ivory tower of medicine breaking down. This marks the beginning of a cultural transformation called digital health. This has also led to a shift in the roles of patients and medical professionals, resulting in a new, equal partnership. When COVID-19 hit, the adoption of digital health technologies skyrocketed. The technological revolution we had been aiming for in health care took place in just months due to the pandemic, but the cultural transition is lagging. This creates a dangerous gap between what is possible technologically through remote care, at-home lab tests, or health sensors and what patients and physicians are actually longing for. If we do it well enough now, we can spare a decade of technological transformations and bring that long-term vision of patients becoming the point of care to the practical reality of today. This is a historic opportunity we might not want to waste.
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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 | 0.036 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".