Creating a pandemic of health: What is the role of digital technologies?
Bibliographic record
Abstract
Imagine a world in which every human being is healthy until the last breath. Thanks to the fast penetration of digital technologies in every region of the planet, this seemingly utopian scenario is not only feasible but also potentially viable. Now that digital technologies have provided almost full interconnectivity among all humans, they should be used to meet key challenges to ensure that health is created and that it spreads to reach every person on earth. The objective of this article is to describe and trigger a serious discussion of such challenges, which include: adopting a new concept of health; positioning self-rated health as the main outcome of the system; creating a health-oriented model to guide service provision; facilitating the identification, scaling up, and sustaining of innovations that can create and spread health; promoting a culture of health promotion; and encouraging the emergence of Precision Health. Once these challenges are met, and health becomes pandemic, public health would have fulfilled its vision, a healthy life for all, at last.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.021 | 0.033 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".