Digital health to support primary care provision during a global pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The urgency of the COVID-19 pandemic in Australia has seen the implementation of digital health technologies to support continuity of high-quality primary care provision. Digital health innovation has been used to operationalise the nation's pandemic preparedness principles by reducing risk of infection to both healthcare workers and at-risk patients, sustaining care for chronic and acute health conditions, and supporting the mental health of the population. In this perspective piece, we document the Australian Federal government's digital health response to ensure the ongoing delivery of high-quality primary care. This includes the implementation of telehealth, point-of-care testing, electronic records and e-prescriptions, national primary care data collection and analysis, and digital communication. Digital health has been a critical element of the pandemic response and paves the way for future primary care provision during disasters and emergencies. Further research is needed to capture the effectiveness, feasibility and acceptability of these innovations for both patients and primary care practitioners.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it