Community Views of Determinants of Men’s Wellbeing in Guatemala: A Study Using Fuzzy Cognitive Mapping
Bibliographic record
Abstract
BackgroundIn post-conflict Guatemala, Indigenous men's psychological distress has been linked to violence exposure, disrupted social support systems, and structural inequities.PurposeWe aimed to document how communities themselves understand men's wellbeing and the factors that influence men's wellbeing.Research design and study sampleFuzzy Cognitive Mapping with 20 stakeholder groups in Santiago Atitlán and Cuilco, Guatemala defined men's wellbeing in local terms and identified the influences community groups understood to promote and detract from men's wellbeing. Participants mapped pathways through which influences affected wellbeing and weighted their relative perceived strength.AnalysisThe researchers used thematic analysis to summarise influences into 43 factors and used fuzzy transitive closure to calculate their net causal influence for each set of stakeholders. We compared perspectives of groups of adult men, adult women, and practitioners of Mayan medicine in Santiago Atitlán, with a primarily Indigenous population, to groups in Cuilco, with a primarily non-Indigenous population. We also compared perspectives across age groups in Santiago Atitlán.ResultsAcross regions, maps highlighted the importance of family and social relations, emotional distress, substance use and physical health for men's wellbeing. Basic resource insecurity and unemployment were top risk factors for men's wellbeing in maps from Cuilco but had both risk and protective influences on men's wellbeing in maps from Santiago Atitlán.ConclusionsFindings challenge the focus on scale-up of individual biomedical interventions as the best strategy to reduce the burden of emotional distress in Guatemala and raise questions about standard development approaches that emphasize income generation and educational attainment above cultural continuity and social harmony.
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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 | 0.038 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.000 | 0.003 |
| 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".