Bibliographic record
Abstract
When there is no vaccine for a disease, 'Test, Trace, Treat/Isolate' is the public health go-to directive. During the COVID-19 pandemic, mobile phone apps are designed to improve on this. But COVID-apps have not been effective as a public health tool. Countries spend millions to develop them, yet they have been shown to have terrible return on investment. This commentary explores why COVID-apps are generally championed and provides three brief case studies (Germany, Sierra Leone, Canada) of non-app public health success. In conclusion, I argue that we need to get our public health care priorities straight: Better and more testing; increased investment in manual contact tracing and treatments; hospitalization when necessary; and wrap-around care - assistance with groceries, cleaning, child- or eldercare responsibilities, telehealth doctor appointment hookups - for sick people in home isolation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.035 | 0.029 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".