Can human connection amplify digital health outcomes? Familial involvement in a mobile health app
Bibliographic record
Abstract
In 2023, Surgeon General Dr. Vivek H. Murthy released an advisory 1 , declaring an unprecedented epidemic of loneliness within the United States. While the advisory outlined numerous factors implicated in the rising rates of social isolation, the expanding role of digital environments garnered particular attention. Proponents of technology cited the unprecedented opportunity for connection and support offered by digital tools, including for the most isolated and vulnerable populations 1 . Older adults, a group disproportionately burdened by loneliness 2 , represent one such population that experienced increased social connection with expanded use of technology 3 . On the other hand, data on the enduring effects of technology on social isolation remain mixed 4 . For example, one study observed increased levels of loneliness in individuals who used social media for the purpose of connection 5 , and a recent review suggested that fewer than half of mobile health interventions focusing on physical activity led to significant improvements in older adults’ experience of loneliness 6 .
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".