Community Level Youth-Led Interventions to Improve Maternal-Neonatal Outcomes in Low- and Middle-Income Countries: Protocol for a Systematic Review
Bibliographic record
Abstract
Introduction. On a global scale, women and childbearing people and neonates continue to die from preventable causes related to pregnancy or childbirth. Sustained and accelerated efforts are critical to improve maternal and neonatal health and well-being. Globally, youth are a growing population and have strength in their numbers. Youth are critical, key drivers of change in their communities. Young people hold the potential to affect positive change, and their meaningful engagement is important to improving maternal health and well-being in low- and middle-income countries. Objectives. To assess the effects of community level youth-led interventions for improving maternal-neonatal health and well-being compared with no interventions or another intervention. Methods. We will undertake a literature search that is comprehensive, complete, and exhaustive. This will include databases such as MEDLINE, EMBASE, and the Cochrane Library, as well as a grey literature search. In our systematic review, we will include experimental studies evaluating maternal-neonatal health and well-being associated with or because of the implementation of community level youth-led interventions. Participants will include women and childbearing people (of any age) during antepartum, intrapartum, and postpartum periods (up to 42 days postpartum). We will examine all interventions addressing and targeting maternal-neonatal health and well-being that are youth-led and community-based and aimed at the members of the community. Our comparators will be no intervention or another intervention. Our primary outcomes are maternal deaths and neonatal deaths. Our review will include only studies in low- and middle-income countries conducted in urban or rural areas. Ethics and Dissemination. Ethics approval is not required as we will use secondary data that is publicly available. There are no active participants in our study. We will involve key stakeholders and experts in maternal-neonatal health regarding dissemination and knowledge mobilization strategies. Our findings will be disseminated as an open access publication, be presented publicly, and defended as part of a doctoral thesis. This trial is registered with CRD42021288798.
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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 | 0.076 | 0.081 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.026 | 0.026 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.078 | 0.011 |
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".