Technology-Based Innovative Solutions for Improving Perinatal Care Utilization: A Network Meta-Analysis
Bibliographic record
Abstract
Abstract Background: Technology-based interventions (TBIs) are widely being tested to integrate into the existing health system to increase preventative healthcare utilizations. Despite an uptake of TBIs, evidence remains inconclusive regarding its effectiveness due to a lack of comprehensive and concrete evaluations. This study aims to evaluate the impact and effectiveness of TBIs in improving antenatal visits, postnatal visits, and delivery care services in LMICs using Bayesian network meta-analysis. Methods: To identify the relevant studies, we will perform a comprehensive search of electronic databases and grey literatures with no language or publication date restriction. We will include experimental studies if they assess effectiveness of TBIs on improving perinatal care utilization among pregnant women. The primary outcomes are antenatal visits, postnatal visits, and delivery care. Two reviewers will independently identify eligible study and perform data extraction and quality assessment. We will use Bayesian meta-analysis to summarize the effect size of individual interventions for each outcome separately. In addition, Bayesian network meta-analyses will be used to pool the direct and indirect effects of all possible comparisons and to provide a hierarchy of all interventions for our primary outcomes. Furthermore, we will assess publication bias and certainty of evidence by GRADE approach for NMA.Discussion: The findings of this review will fill the gaps in evidence by synthesizing the impact of TBIs on a comprehensive range of reported outcomes in antenatal, postnatal, and delivery care. Furthermore, our meta-analyses will further guide policymakers or stakeholders on the potential use of TBIs in given contexts. Systematic review registration: PROSPERO Protocol ID CRD42019137331
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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 | 0.042 | 0.081 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.066 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".