Assessing the performance of the family folder system for collecting community-based health information in Tigray Region, North Ethiopia: a capture–recapture study
Bibliographic record
Abstract
OBJECTIVES: To assess completeness and accuracy of the family folder in terms of capturing community-level health data. STUDY DESIGN: A capture-recapture method was applied in six randomly selected districts of Tigray Region, Ethiopia. PARTICIPANTS: Child health data, abstracted from randomly selected 24 073 family folders from 99 health posts, were compared with similar data recaptured through household survey and routine health information made by these health posts. PRIMARY AND SECONDARY OUTCOME MEASURES: Completeness and accuracy of the family folder data; and coverage selected child health indicators, respectively. RESULTS: Demographic data captured by the family folders and household survey were highly concordant, concordance correlation for total population, women 15-49 years age and under 5-year child were 0.97 (95% CI 0.94 to 0.99, p<0.001), 0.73 (95% CI 0.67 to 0.88) and 0.91 (95% CI 0.85 to 0.96), respectively. However, the live births, child health service indicators and child health events were more erratically reported in the three data sources. The concordance correlation among the three sources, for live births and neonatal deaths was 0.094 (95% CI -0.232 to 0.420) and 0.092 (95% CI -0.230 to 0.423) respectively, and for the other parameters were close to 0. CONCLUSION: The family folder system comprises a promising development. However, operational issues concerning the seamless capture and recording of events and merging community and facility data at the health centre level need improvement.
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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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".