Use of Mobile Phone to Promote Governance and Equity within the Health System: Experience of Rural Health District in Burkina Faso
Bibliographic record
Abstract
Background: Mobile phone has been described as offering a remarkable potential to deliver primary health care. It is widely used in low-and middle-income countries to support health care delivery. In Burkina Faso, high maternal mortality rates and persistent numbers of people living with HIV are priorities to address by government. Here we described an innovative mobile phone platform that helps to overcome barriers of access to health service by community members in rural health district. Methods: A mobile phone project was implemented to enhance better access to health information and care delivery for mother, and people living with HIV. An interactive messaging and voice system was developed and incorporated major local languages to overcome literacy barrier. In addition, a patient’s reminder system for follow-up appointments was incorporated. Results: Overall 423 pregnant women, 319 new-born mothers and 116 HIV/AIDS patients were followed-up by the system in 2015 by community health workers. An average 177 patient’s reminder for appointment was completed. There was an 8% increase of antenatal care uptake and better compliance of HIV patients to antiretroviral services. There was also a reduction of about 84% of loss of followed-up from HIV patients (P<0,05), and an increase of 41% of assisted deliveries. However, running mobile devices in a hard to reach population is challenging. Conclusion: Use of mobile phones at a community level is a powerful tool to increase their equitable access to health care information and participation to local health care governance. However challenges must be anticipated.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".