A review on mHealth research in developing countries
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
Governments and development agencies are advocating mobile technology as a potential tool for developing and improving livelihoods, especially in developing countries where traditional technologies have failed to gain ground for wide ranging reasons. It is, therefore, understandable that the use of mobile technology in health care (mHealth) is growing in developing countries. Healthcare is one of the challenges facing developing countries, with the majority of the countries still lagging behind in most of the health related Millennium Development Goals (MDG) (Goals 4, 5 and 6). Due to the nascence of the domain, research in the domain is still in its infancy and, as such, there is little evidence to support the claims about the impact of the technology. The aim of this paper is to analyse the progress of mHealth as well as the progress of the research in the domain in developing countries. Data for the study are mHealth papers presented at the Third Mobile for Development (M4D) Conference which took place in India between 28th and 29th February 2012. The review notes the following about research in mHealth in developing countries: (i) Most interventions are patient-facing; this provides opportunities for using mHealth to empower the public; (ii) The interventions use a growing range of technological solutions; (iii) Most research still focuses on pilot projects as opposed to scaled-up projects and (iv) Research in the domain still lacks rigour.
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 | 0.071 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.016 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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