Future of digital health and community care: Exploring intended positive impacts and unintended negative consequences of COVID-19
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
Response to COVID-19 has both intentionally and unintentionally progressed the digitization of health and community care, which can be viewed as a human rights issue considering that access to health and community care is a human right. In this article, we reviewed two cases of digitization of health and community care during the pandemic; one in Scotland, United Kingdom and another in British Columbia, Canada. An integrated analysis revealed that digitization of health and community care has intended positive and unintended negative consequences. Based on the analysis, we suggest five areas of improvement for equity in care: building on the momentum of technology advantages; education and digital literacy; information management and security; development of policy and regulatory frameworks; and the future of digital health and community care. This article sheds light on how health practitioners and leaders can work to enhance equity in care experiences amid the changing digital landscape.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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