Community perceptions about causes of suicide among young men in Botswana: an analysis based on fuzzy cognitive maps
Bibliographic record
Abstract
Suicide is common in Botswana, particularly among young men. Fuzzy cognitive mapping (FCM) can support participatory research by depicting local stakeholder knowledge about causes of health outcomes. This study used FCM to explore local perceptions about causes of suicide among young men in rural communities close to the capital, Gaborone. In nine sessions, groups of young men, young women, older men, and older women separately mapped their knowledge of factors related to suicide among young men (46 people in total). Two trained facilitators, fluent in the local language, led the group sessions. The maps depicted risk and protective factors as nodes connected by arrows to show causal relationships. Participants also ranked the strength of each link on a scale of one (weakest) to five (strongest). Fuzzy transitive closure calculated the maximum influence of each factor, taking into account all other influences on the map. We combined maps by different stakeholders and grouped the 130 unique factors across the maps into 17 broader categories which emerged from an inductive thematic analysis of all the node labels. Financial difficulties, relationship problems, and family issues were the strongest categories of perceived causes of suicide by young men. Mental health problems played an intermediary role between more distal causes and suicide. There were differences in maps of different gender and age groups, but the strongest influences were consistent across groups. Young women, but not young men, identified men’s lack of self-esteem as a strong cause of suicide. The FCM findings offer a starting point for community discussions to seek local solutions to youth suicide.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".