Reasons for Not Reporting Deaths: A Qualitative Study in Rural Vietnam
Bibliographic record
Abstract
This qualitative study explores socio-cultural and health systems factors that may impact on death reporting by lay people to registry systems at the commune level. Information on local perceptions of death and factors influencing death reporting were gathered through nine focus group discussions with people of different religions and ethnic affiliations in a rural district of northern Vietnam. Participants classified deaths as "elderly deaths," "young deaths," and "child deaths." Child deaths, including newborn deaths, used to be considered punishment for sins committed by ancestors, but this is no longer the case. Concepts of the human soul and afterlife differ between the Catholic and Buddhist groups, influencing funeral rituals and reporting, especially of infant deaths. Participants regarded elderly deaths as "natural" and "deserved," while young deaths were seen as either "good deaths" or "bad deaths." "Bad deaths" were defined as deaths of "dishonourable" persons who had led a "bad life" involving activities such as gambling, drinking or stealing. The causes of "bad deaths" and deaths due to stigmatized diseases (e.g., HIV/AIDS, tuberculosis and leprosy) were often concealed by the family. The study suggests that the risk of under-reporting deaths seems to be largest for deaths of infants and "bad deaths." Little awareness of regulations and lack of incentives for reporting or lack of sanctions for not reporting deaths also result in under-reporting of deaths. Therefore, education programs and enforcement of legal regulations on death notification should be emphasized. The risk of misreporting the real causes of "bad deaths" and deaths due to stigmatized diseases should be considered in verbal autopsy interviews. Using different sources of information (triangulation) is useful in order to minimize both under-registration and misreporting causes of death.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".