A review on mHealth research in developing countries
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".