Community perceptions of health and rodent-borne diseases along the Inter-Oceanic Highway in Madre de Dios, Peru
Bibliographic record
Abstract
BACKGROUND: Madre de Dios is located in the southeastern Amazonian region of Peru. Rodents have been estimated to be the reservoirs for up to 50 % of emerging zoonotic pathogens, including a host of viruses, bacteria, and parasites. As part of a larger study involving both human and animal research, this study serves to obtain a broader understanding of the key challenges and concerns related to health and rodent-borne illnesses from the perspective of the people living in these communities. METHODS: We used a mixed methods approach, which comprised of 12 focus group discussions, 34 key informant interviews and the application of a survey (n = 522) in four communities along the Inter-Oceanic Highway (IOH) in Madre de Dios, Peru over a two-year period. RESULTS: Although 90 % of survey respondents answered that rodents can transmit diseases and had seen rodents in their homes and immediate surroundings, most could not name specific rodent-borne diseases and, when probed, described rodents as pests or nuisance animals, but were not concerned about acquiring illnesses from them. Key informant interview data suggests that there has been a perceived increase in the amount of rodents in the communities since the construction of the IOH, however this potential increase was not coupled with increased knowledge about diseases or perceived risks among these key informants. Health providers also mentioned a lack of diagnostic tools specific for rodent-borne illnesses. This may be related to the fact that although a common rodent-borne disease like leptospirosis is frequently detected in the region, it is not routinely and readily diagnosed, therefore the real burden of the disease and exposure risk can be underestimated. If rodent-borne diseases are not on the radar of health professionals, they may not consider presumptive treatment, which could result in unnecessary morbidity and mortality. CONCLUSION: Awareness of rodent-borne diseases is still lacking in the area, even among health care professionals within the communities, despite the known burden of diseases like leptospirosis. We expect to report further findings as we obtain more information from all the study components.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".