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Record W4298140069 · doi:10.1186/s12913-022-08589-9

The contribution of social norms and religious practices towards low death registration in 3 HDSS sites of Uganda

2022· article· en· W4298140069 on OpenAlexfundno aff
Gilbert Habaasa

Bibliographic record

VenueBMC Health Services Research · 2022
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
FundersInternational Union for the Scientific Study of PopulationGlobal Affairs CanadaInternational Development Research Centre
KeywordsFocus groupMedicineQualitative researchSociologySocial science

Abstract

fetched live from OpenAlex

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 religious practices that could have contributed to low death registration in Uganda. Previous studies on the factors affecting death registration have not assessed the contribution of social norms and religious practices toward low death registration in developing countries. METHODS: A qualitative study design was adopted to examine the contribution of social norms and religious practices toward low death registration in the 3 Health and Demographic Surveillance systems (HDSS) sites of Uganda. The methods of data collection included: focus group discussions, key informant interviews, and a document review of the death registration booklet. 6 FGDs, 2 from each HDSS site were conducted comprising 1 female FGD of 10 participants and 1 male FGD of 10 participants. In addition, 26 key informant interviews were conducted with the district leaders, local council leaders, health care workers, cultural leaders, elderly, HDSS scouts and religious leaders in the 3 HDSS sites. RESULTS: December 2020. The study shows that social norms and religious practices have contributed to the low death registration in the 3 HDSS sites in Uganda. Social norms and religious practices either hinder or discourage death registration initiatives by the government of Uganda. It was found out that burials that take place on the same day of death discourage death registration. Cultural taboo to announcing the death of infants, neonates, twins and suicides in the community hinder death registration. The burying of a woman at her parent's house after bride price payment default by the family of a husband discourages death registration. The religious institutions have their own set of rules, practices, and norms, which in most cases discourage death registration. For example, religious leaders refuse to lead funeral prayers for non-active members in religious activities. Results also showed that mixed religions in families bring about conflicts that undermine death registration. Lastly, results showed that traditionalists do not seek medical treatment in hospitals and this hinders death registration at the health facilities. CONCLUSION: The study shows that death registration is very low in the 3 HDSS sites in Uganda and that social norms and religious practices contribute greatly to the low death registration. To overcome the negative effects of social norms and religious practices, a social behaviour campaign is proposed. In addition, community dialogue should be conducted to identify all negative social norms and religious practices, how they are perpetuated, their effects, and how they can be renegotiated or eliminated to bring about high death registration in the 3 HDSS sites of Uganda. Lastly, there is a need for partnerships with cultural and religious leaders to sensitize community members on the effect of social norms and religious practices on low death registration in the 3 HDSS sites in Uganda.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.003
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.047
GPT teacher head0.438
Teacher spread0.391 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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