Protocol for a systematic review on inequalities in postnatal care services utilization in low- and middle-income countries
Bibliographic record
Abstract
BACKGROUND: Each year, 287,000 women die from complications related to pregnancy or childbirth, and 3.8 million newborns die before reaching 28 days of life. The near totality (99%) of maternal and neonatal deaths occurs in low- and middle-income countries (LMICs). Utilization of essential obstetric care services including postnatal care (PNC) largely contributes to the reduction of maternal and neonatal mortality and morbidity. There is a strong need to evaluate the evidence on the unmet needs in utilization of PNC services to inform health policy planning. Our objective is to assess systematically the socioeconomic, geographic and demographic inequalities in the use of PNC interventions in low- and middle-income countries. METHODS/DESIGN: The current protocol adopts a strategy informed by the guidelines of The Cochrane Handbook for Systematic Reviews. Our systematic review will identify studies in English, French, Spanish, Portuguese and Chinese - provided inclusion of an English abstract - from 1960 onwards, by searching MEDLINE (PubMed interface), EMBASE (OVID interface), Cochrane Central (OVID interface) and the gray literature. Study selection criteria include research setting, study design, reported outcomes and determinants of interest. Our primary outcome is the utilization of PNC services, and determinants of concern are: 1) socioeconomic status (for example, income, education); 2) geographic determinants (for example, distance to a health center, rural versus urban residence); and 3) demographic determinants (for example, ethnicity, immigration status). Screening, data abstraction, and scientific quality assessment will be conducted independently by two reviewers using standardized forms. Where feasible, study results will be combined through meta-analyses to obtain a pooled measure of association between utilization of PNC services and key determinants. Results will be stratified by countries' income levels (World Bank classification). DISCUSSION: Our review will inform policy-making with the aim of decreasing inequalities in utilization of PNC services. This research will provide evidence on unmet needs for PNC services in LMICs, knowledge gaps and recommendations to health policy planners. Our research will help promote universal coverage of quality PNC services as an integral part of the continuum of maternal and child health care. This protocol was registered with the Prospero database (registration number: CRD42013004661).
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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 | 0.086 | 0.122 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.023 | 0.022 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.108 | 0.014 |
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".