Mobile health for cancer in low to middle income countries: priorities for research and development
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
Many current global health opportunities have less to do with new biomedical knowledge than with the coordination and delivery of care. While basic research remains vital, the growing cancer epidemic in countries of low and middle income warrants urgent action - focusing on both research and service delivery innovation. Mobile technology can reduce costs, improve access to health services, and strengthen health systems to meet the interrelated challenges of cancer and other noncommunicable diseases. Experience has shown that even very poor and remote communities that only have basic primary health care can benefit from mobile health (or 'mHealth') interventions. We argue that cancer researchers and practitioners have an opportunity to leverage mHealth technologies that have successfully targeted other health conditions, rather than reinventing these tools. We call for particular attention to human centred design approaches for adapting existing technologies to suit distinctive aspects of cancer care and to align delivery with local context - and we make a number of recommendations for integrating mHealth delivery research with the work of designers, engineers and implementers in large-scale delivery programmes.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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