Village registers for vital registration in rural <scp>M</scp>alawi
Bibliographic record
Abstract
Paper-based village registers were introduced 5 years ago in Malawi as a tool to measure vital statistics of births and deaths at the population level. However, usage, completeness and accuracy of their content have never been formally evaluated. In Traditional Authority Mwambo, Zomba district, Malawi, we assessed 280 of the 325 village registers with respect to (i) characteristics of village headmen who used village registers, (ii) use and content of village registers, and (iii) whether village registers provided accurate information on births and deaths. All village headpersons used registers. There were 185 (66%) registers that were regarded as 95% completed, and according to the registers, there were 115 840 people living in the villages in the catchment area. In 2011, there were 1753 births recorded in village registers, while 6397 births were recorded in health centre registers in the same catchment area. For the same year, 199 deaths were recorded in village registers, giving crude death rates per 100 000 population of 189 for males and 153 for females. These could not be compared with death rates in health centre registers due to poor and inconsistent recording in these registers, but they were compared with death rates obtained from the 2010 Malawi Demographic Health Survey that reported 880 and 840 per 100 000 for males and females, respectively. In conclusion, this study shows that village registers are a potential source for vital statistics. However, considerable inputs are needed to improve accuracy of births and deaths, and there are no functional systems for the collation and analysis of data at the traditional authority level. Innovative ways to address these challenges are discussed, including the use of solar-powered electronic village registers and mobile phones, connected with each other and the health facilities and the District Commissioner's office through the cellular network and wireless coverage.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".