Addressing the Evidence Gap in the Economic and Social Benefits of Civil Registration and Vital Statistics Systems: A Systematic Review
Bibliographic record
Abstract
Objectives: Considering the aspiration embedded in the Sustainable Development Goals to Leave No One Behind by 2030, civil registration and vital statistics systems have an essential role in providing reliable, up-to-date information to monitor the progress. Thus, the aim of this systematic review is to compile empirical evidence on the benefits of a functioning civil registration and vital statistics system. Methods: Selected databases were systematically searched until 2019. Key experts were also contacted for relevant literature. The review process was managed with the software EPPI-Reviewer and followed standard methods for systematic reviews. Results: A total of 18 studies were included. The findings revealed that having birth, death, and/or marriage registration, and vital statistics were associated with access to rights and protection, positive impact on economic and health outcomes, and increased access to education. Conclusion: The present review supports the idea that systemic approaches strengthen civil registration and vital statistics systems due to the cumulative effects of vital events’ registration. Ensuring appropriate systems for civil registration will have an impact not only on the individuals but also on the generations to come.
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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 | 0.020 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".