Causes of short birth interval (kunika) in Bauchi State, Nigeria: systematizing local knowledge with fuzzy cognitive mapping
Bibliographic record
Abstract
BACKGROUND: Short birth intervals, defined by the World Health Organization as less than 33 months, may damage the health and wellbeing of children, mothers, and their families. People in northern Nigeria recognise many adverse effects of short birth interval (kunika in the Hausa language) but it remains common. We used fuzzy cognitive mapping to systematize local knowledge of causes of kunika to inform the co-design of culturally safe strategies to address it. METHODS: Male and female groups in twelve communities built 48 maps of causes and protective factors for kunika, and government officers from the Local Government Area (LGA) and State made four maps. Each map showed causes of kunika or no-kunika, with arrows showing relationships with the outcome and between causes. Participants assigned weights for the perceived strength of relationships between 5 (strongest) and 1 (weakest). We combined maps for each group: men, women, and government officers. Fuzzy transitive closure calculated the maximum influence of each factor on the outcome, taking account of all relationships in the map. To condense the maps, we grouped individual factors into broader categories and calculated the cumulative net influence of each category. We made further summarised maps and presented these to the community mapping groups to review. RESULTS: The community maps identified frequent sex, not using modern or traditional contraception, and family dynamics (such as competition between wives) as the most influential causes of kunika. Women identified forced sex and men highlighted lack of awareness about contraception and fear of side effects as important causes of kunika. Lack of male involvement featured in women's maps of causes and in the maps from LGA and State levels. Maps of protective factors largely mirrored those of the causes. Community groups readily appreciated and approved the summary maps resulting from the analysis. CONCLUSIONS: The maps showed how kunika results from a complex network of interacting factors, with culture-specific dynamics. Simply promoting contraception alone is unlikely to be enough to reduce kunika. Outputs from transitive closure analysis can be made accessible to ordinary stakeholders, allowing their meaningful participation in interpretation and use of the findings. For people in Bauchi State, northern Nigeria, kunika describes a short interval between successive births, understood as becoming pregnant again before the previous child is weaned. They recognise it is bad for children, mothers and households. We worked with 12 communities in Bauchi to map their knowledge of the causes and protective factors for kunika. Separate groups of men and women built 48 maps, and government officers at local and state level built four maps. Each group drew two maps showing causes of kunika or of no-kunika with arrows showing the links between causes and the outcome. Participants marked the strength of each link with a number (between 5 for the strongest and 1 for the weakest). We combined maps for women, men and government officers. We grouped similar causes together into broader categories. We calculated the overall influence of each category on kunika or no-kunika and produced summary maps to communicate findings. The maps identified the strongest causes of kunika as frequent sex, not using modern or traditional contraception, and family dynamics. Women indicated forced sex as an important cause, but men focused on lack of awareness about contraception and fear of side effects. The maps of protective factors mirrored those of the causes. The groups who created the maps approved the summary maps. The maps showed the complex causes of kunika in Bauchi. Promoting contraception is unlikely to be enough on its own to reduce kunika. The summary maps will help local stakeholders to co-design culturally safe ways of reducing kunika.
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 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".