Perception of Mobile Health Maternal Healthcare Services among Pregnant Women in Nigeria
Bibliographic record
Abstract
The daunting challenge of maternal deaths resulting from preventable causes has remained a major public health issue in the developing countries of Africa and it is yet to be fully tackled. Empirical knowledge of the awareness of mobile health (m-health) maternal healthcare services among pregnant women in these parts, therefore, holds an important key to achieving success in reproductive health issues across the affected populations. Despite the introduction of mobile health services, the desirable state of reduced maternal mortality figures is yet to be achieved. This study sought to ascertain the level of awareness, extent of adoption and the challenges of m-health maternal services in southeast Nigeria. Our analysis of questionnaire-survey data on pregnant women from three states of Nigeria shows that 89.7% of respondents were aware of m-health in the study areas. However, awareness of the existence of m-health for maternal healthcare is different from usage of m-health in maternal healthcare delivery. The major challenges to its use are network failure from service providers and lack of funds for subscription, which may also mean that mobile phone ownership alone does not determine the success of m-health maternal health services in these parts. This leads to one of the recommendations that those who design mobile health application should consider offline mechanism as an alternative to the recurring network failure, for ease of use of such technology.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".