Exploring the interconnections between health, climate crisis, food insecurity and institutional neglect in Alta Verapaz region, Guatemala
Bibliographic record
Abstract
Introduction: Rural Indigenous communities in Alta Verapaz, Guatemala face escalating and multifaceted health risks due to recurrent extreme climate events. This article focuses on the deepening crisis of chronic food insecurity and malnutrition, driven both by acute shortages during climate shocks and the long-term degradation of local food systems. These harms are further compounded by entrenched structural inequalities and limited access to government emergency response systems and public institutions more broadly. Methods: This study draws on participatory action research conducted with 16 Maya Q'eqchi' communities and civil society partners. Data were collected through participatory mapping, group dialogues, and institutional analysis, and were analyzed using thematic methods grounded in the social determination of health framework. Results: The study identifies two central concerns: the intensification of food insecurity driven by both climate change and the expansion of monoculture agriculture, and the inadequate institutional response to these interrelated crises. Community members reported crop loss, declining soil fertility, toxic contamination following floods, and ongoing encroachment on their habitats. National policy analysis reveals that, although the emergency response system appears adequate in design, its implementation is hindered by limited capacity and chronic under-resourcing at the community level. Conclusion: The interplay of climate shocks, food system pressures, and institutional failure requires a rights-based, multilevel approach to health and climate justice. Public investment, decentralized emergency planning, and recognition of Indigenous knowledge are critical parts of addressing structural drivers of vulnerability. Community-led strategies must be supported by responsive, well-resourced public institutions.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".