Prevalence and factors associated with undocumented children under-five in Haiti
Bibliographic record
Abstract
BACKGROUND: Despite many efforts to provide children with legal existence over the last decades, 1 in 4 children under the age of 5 (166 million) do not officially exist, with limited possibility to enjoy their human rights. In Latin America and the Caribbean, Haiti has one of the highest rates of undocumented births. This study aimed to analyze the prevalence and the determinant factors of undocumented childhood in Haiti. METHODS: For analysis of undocumented childhood and related socioeconomic determinants, data from the 2016/17 Haiti demographic and health survey were used. The prevalence and the associated factors were analyzed using descriptive statistics and the binary logistic regression model. RESULTS: The prevalence of undocumented childhood in Haiti was 23% (95% CI: 21.9-24.0) among children under-five. Among the drivers of undocumented births, mothers with no formal education (aOR = 3.88; 95% CI 2.21-6.81), children aged less than 1 year (aOR = 20.47; 95% CI 16.83-24.89), children adopted or in foster care (aOR = 2.66; 95% CI 1.67-4.24), children from the poorest regions like "Artibonite" (aOR = 2.19; 95% CI 1.63-2.94) or "Centre" (aOR = 1.51; 95% CI 1.09-2.10) or "Nord-Ouest" (aOR = 1.61; 95% CI 1.11-2.34), children from poorest households (aOR = 6.25; 95% CI 4.37-8.93), and children whose mothers were dead (aOR = 2.45; 95% CI 1.33-4.49) had higher odds to be undocumented. CONCLUSION: According to our findings, there is an institutional necessity to bring birth documentation to underprivileged households, particularly those in the poorest regions where socioeconomic development programs are also needed. Interventions should focus on uneducated mothers who are reknown for giving birth outside of medical facilities. Therefore, an awareness campaign should be implemented to influence the children late-registering behavior.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".