Likely Health Impacts of Climate Change in Guyana: A Systematic Review
Bibliographic record
Abstract
As anthropogenic inputs continue to drive climate change towards a "tipping point" of increasingly severe consequences, associated research has become more important than ever. Even if mitigation efforts are successful in slowing, or even halting, climate change progression, changes have already been triggered that will be felt for decades; the health impacts of these changes will be felt in most populations around the world and will threaten the well-being of billions. Further, it has been suggested that these impacts will be experienced differently, especially depending on geography. As such, it is crucial for location-specific analysis of the potential consequences of climate change to take place. This study constitutes a systematic review of the health consequences that can be expected in Guyana specifically, the results of which are of some relevance to Latin American more generally. Relevant documents selected for full review underwent quantitative and qualitative data analysis. From this analysis, six thematic categories emerged: i) dengue and malaria, ii) other infections, iii) flooding and waterborne diseases, iv) food and water shortages, v) respiratory issues, and vi) natural disasters. These represent the most likely and most severe health consequences that may be exacerbated by climate change impacts in Guyana. Despite these insights, a knowledge and research gap in this field is evident, and a call is made for further research and policy action to better understand and prepare for the upcoming challenges climate change will present.
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".