Impact of mobile health on maternal and child health service utilization and continuum of care in Northern Ghana
Bibliographic record
Abstract
Maternal and child mortality are of public health concern. Most of these deaths occur in rural communities of developing countries. Technology for maternal and child health (T4MCH) is an intervention introduced to increase Maternal and Child Health (MCH) services utilization and continuum of care in some health facilities across Ghana. The objective of this study is to assess the impact of T4MCH intervention on MCH services utilization and continuum of care in the Sawla-Tuna-Kalba District in the Savannah Region of Ghana. This is a quasi-experimental study with a retrospective review of records of MCH services of women who attended antenatal services in some selected health centers in the Bole (comparison district) and Sawla-Tuna-Kalba (intervention district) of the Savannah region, Ghana. A total of 469 records were reviewed, 263 in Bole and 206 in Sawla-Tuna-Kalba. A multivariable modified Poisson and logistic regression models with augmented inverse-probability weighted regression adjustment based on propensity scores were used to quantify the impact of the intervention on service utilization and continuum of care. The implementation of T4MCH intervention increased antenatal care attendance, facility delivery, postnatal care and continuum of care by 18 percentage points (ppts) [95% CI - 17.0, 52.0], 14 ppts [95% CI 6.0%, 21.0%], 27 ppts [95% CI 15.0, 26.0] and 15.0 ppts [95% CI 8.0, 23.0] respectively compared to the control districts. The study showed that T4MCH intervention improved antenatal care, skilled delivery, postnatal services utilization, and continuum of care in health facilities in the intervention district. The intervention is recommended for a scale-up in other rural areas of Northern Ghana and the West-African sub-region.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".