Maternal Health Care Information for Improving Access to Maternal Health Services for Adolescent Girls and Young Women (AGYW) in Lower-and-Middle Income Countries
Bibliographic record
Abstract
BACKGROUND: There is a dire need to uplift the well-being of pregnant adolescent girls and young women through development and implementation of strategies that can reduce barriers to access maternal healthcare services. This in return will reduce pregnancy related complications which is a threat to many adolescents especially in developing countries. FINDINGS: Other stakeholders such as community members are less involved in the activities of AGYW. Information is not enough to cater for the health needs of AGYW. There are less policies and law in place that will guide the AGYW to make informed decisions. Young people, need to be provided with quality education, training, health information and services to enable them to make informed decisions. Furthermore, their rights need to be protected. They also need to be granted with opportunities to participate in decision making and to equip them for the future. This will enable them to participate in the affairs of their communities and contribute to economic development (UNFPA, 2014, 2017b; WHO, 2015a). CONCLUSION: Poor access to maternal health care information by young women may lead to poor use of maternal healthcare services and maternal outcomes. Maternal healthcare information have been used as a strategy to improve maternal healthcare services to reduce the risk of complications during pregnancy. This review therefore provided an overview on healthcare information that enable access and utilize of maternal healthcare services by adolescent and young women during pregnancy in Lower-and-Middle Income Countries (LMICs). Barriers relating to accessing and utilizing maternal healthcare services by young women in LMICs were also discussed. This review has additionally recommended creative interventions for conquering these obstructions to help improve maternal health outcomes in Lower and Middle Income Countries.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".