Shared perceived causes of suicide among young men and violence against young women offer potential for co-designed solutions: intervention soft-modelling with fuzzy cognitive mapping
Bibliographic record
Abstract
Violence against young women (VAW) and suicide among young men are serious concerns in Botswana and elsewhere. We examined the overlap in locally perceived causes of these two forms of violence in Botswana using the results from separate studies that used fuzzy cognitive mapping (FCM) to explore perceived causes of the two outcomes. FCM depicts perceived causes of an outcome and their links to the outcome and each other, with weights denoting the perceived strength of each link. The two studies engaged groups of young women, young men, older women, and older men in rural communities. We grouped related concepts into broader categories, then combined category maps for each outcome into a single map including both forms of violence. Based on social network analysis, we calculated the out-degree centrality of each category indicating its influence within the network. Intervention soft-modelling explored the effects of removing individual categories on suicide and VAW. Of 24 causal categories in the combined map, six were shared between both outcomes, 10 were for suicide only, and seven were for VAW only. The six shared categories accounted for 60% of cumulative influence of all categories in the combined map. The three most influential shared categories were financial difficulties, conflict in relationships, and parenting and family issues. Based on local perceptions, avoiding conflict in relationships could reduce suicide by 4.8% and VAW by 18.5%. Eliminating parenting and family issues could reduce suicide by 3% and VAW by 5.4%. Preventing financial difficulties could reduce suicide by 9.3% and VAW by 2.9%. The findings support the idea that some interventions might reduce both personal and interpersonal violence among youth. Analysis of stakeholder perceived causes and soft-modelling of potential interventions could inform community-led co-design of strategies to reduce youth suicide and violence against young women in Botswana.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".