The Contribution of Social Norms and Behavioural Practices towards Low Death Registration in 3 HDSS Sites of Uganda
Bibliographic record
Abstract
Abstract Background: Uganda has low levels of death registration, estimated at two per cent by the National Identification and Registration Authority (NIRA). There are 56 tribes and over 5 religious denominations with so many social norms and behavioural practices that could have contributed towards low death registration in Uganda. Previous studies on the factors affecting death registration have not assessed the contribution of social norms and behavioural practices towards low death registration in developing countries. Methods: The current research has been conducted in 3 Health and Demographic Surveillance System (HDSS) sites of Uganda to fill this knowledge gap. The study adopted a qualitative approach by conducting 26 interviews, 6 focus group discussions, and a document review. The study targeted men, women, district local government leaders, hospital administrators, religious leaders, cultural leaders, village health teams, HDSS village scouts, and opinion leaders. Results: There is very low death registration rates in the Iganga-Mayuge, Kyamulibwa, and Rakai HDSS sites and also expounded on how social norms and behavioural practices either hinder or discourage death registration initiatives by the government of Uganda. Some of these norms include informal will-making and inheritance sharing and taboos to announce the death of twins, infants, neonates, and even suicides. Religious institutions have their own set of rules, practices, and norms, which in most cases discourage death registration—for example, praying for the dead only when they or their relatives have been actively engaged in religious activities. Conclusion: The study confirms that social norms and behavioural practices contribute to low death registration in the 3 HDSS sites of Uganda. The civil registration authorities in Uganda, therefore, need to recognize the heterogeneity of cultural norms, religious beliefs and practices around death and how these influence the propensity for different deaths to be registered.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".