An mHealth Framework to Improve Birth Outcomes in Benue State, Nigeria: A Study Protocol
Bibliographic record
Abstract
BACKGROUND: The unprecedented coverage of mobile technology across the globe has led to an increase in the use of mobile health apps and related strategies to make health information available at the point of care. These strategies have the potential to improve birth outcomes, but are limited by the availability of Internet services, especially in resource-limited settings such as Nigeria. OBJECTIVE: Our primary objective is to determine the feasibility of developing an integrated mobile health platform that is able to collect data from community-based programs, embed collected data into a smart card, and read the smart card using a mobile phone-based app without the need for Internet access. Our secondary objectives are to determine (1) the acceptability of the smart card among pregnant women and (2) the usability of the smart card by pregnant women and health facilities in rural Nigeria. METHODS: We will leverage existing technology to develop a platform that integrates a database, smart card technology, and a mobile phone-based app to read the smart cards. We will recruit 300 pregnant women with one of the three conditions-HIV, hepatitis B virus infection, and sickle cell trait or disease-and four health facilities in their community. We will use Glasgow's Reach, Effectiveness, Adoption, Implementation, and Maintenance framework as a guide to assess the implementation, acceptability, and usability of the mHealth platform. RESULTS: We have recruited four health facilities and 300 pregnant women with at least one of the eligible conditions. Over the course of 3 months, we will complete the development of the mobile health platform and each participant will be offered a smart card; staff in each health facility will receive training on the use of the mobile health platform. CONCLUSIONS: Findings from this study could offer a new approach to making health data from pregnant women available at the point of delivery without the need for an Internet connection. This would allow clinicians to implement evidence-based interventions in real time to improve health outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT03027258; https://clinicaltrials.gov/ct2/show/NCT03027258 (Archived by WebCite at http://www.webcitation.org/6owR2D0kE).
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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 | 0.026 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".