Sociocultural descriptions of febrile syndromes in rural areas of Urabá, Colombia: An exploration of “tick fever”
Bibliographic record
Abstract
Abstract Introduction: In 2006 and 2008 there were two lethal outbreaks of rickettsioses in the rural areas of Urabá, characterized by the lack of immediate diagnosis and antibiotic treatment. Objective: Describe sociocultural aspects about knowledge, attitudes and practices in relation to febrile syndromes and “tick fever” in rural areas of Urabá. Materials and methods: We conducted an exploratory study using knowledge, attitudes, and practices questionnaires and semi-structured interviews about febrile syndromes and “tick fever”. We surveyed 246 heads of households and interviewed nine individuals. Results: We observed that people tended to identify febrile syndromes with signs and clinical symptoms of dengue, malaria, leptospirosis and rickettsioses. A considerable proportion of individuals (32.93%) knew very little about “tick fever”, thinking that is was transmitted by mosquitos. They mentioned intestinal parasitoids, malaria, dengue, and “evil eye” among the causes of febrile syndromes. “Tick fever” is linked by its name to the bite of the tick. Furthermore, the treatments for febrile syndromes mentioned by interviewees are associated to those commonly used in western medicine and medicinal plants. Conclusions: There is a need for educational programs in rural areas, to raise awareness about these potential lethal conditions that can be effectively treated.
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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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".