Digital psychiatry in low- and middle-income countries post-COVID-19: Opportunities, challenges, and solutions
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
Health systems are adapting to the unique challenges posed by the COVID-19 pandemic. Social distancing has forced clinicians to provide their services through online platforms in high income countries. Similar trends have been noticed in Low and middle-income countries (LAMIC). Digital health can help LAMIC address traditional barriers to care by overcoming issues related to stigma, discrimination, staffing, and physical and geographical resource constraints. Mobile phone subscriptions exceed 80% of the population in many LAMICs. Mobile platforms represent a viable resource in overcoming the significant mental health gap in LAMIC. This paper discusses the enormous potential that digital health has to transform healthcare delivery in LAMICs, as well as numerous challenges to implementation. We also discuss the need to develop national digital health strategies and suggest solutions to some of the barriers.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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