Motivations and barriers to death registration in Dakar, Senegal
Bibliographic record
Abstract
Abstract Strengthening civil registration systems requires a better understanding of motivations and barriers related to the registration of deaths. We used data from the 2013 Senegalese census to identify deaths that are more likely to be registered in the Dakar region, where the completeness of death registration is higher than 80%. We also interviewed relatives of the deceased whose death had been registered to collect data on reasons for registration and sources of information about the process. The likelihood of death registration was positively associated with age at death and household wealth. Death registration was also more likely in households whose head was older, had attended school, and had a birth certificate. At the borough commune level, the geographical accessibility of civil registration centres and population density were both positively associated with completeness of death registration. The main motivations for registering deaths were compliance with the legal obligation to do so and willingness to obtain a burial permit and a death certificate. Families, health facilities, and friends were the primary sources of information about death registration. Further research is needed to identify effective interventions to increase death registration completeness in Dakar, particularly amongst the poorest households and neighbourhoods on the outskirts of the city.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".