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Record W2990946708 · doi:10.1590/s0103-73312019290319

Sociocultural descriptions of febrile syndromes in rural areas of Urabá, Colombia: An exploration of “tick fever”

2019· article· en· W2990946708 on OpenAlexaff
Juan Carlos Quintero Vélez, Javier Mignone, Lisardo Osório Quintero, Carlos Rojas

Bibliographic record

VenuePhysis Revista de Saúde Coletiva · 2019
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Vectors
Canadian institutionsUniversity of Manitoba
FundersUniversidad de Antioquia
KeywordsDengue feverMalariaLeptospirosisOutbreakMedicineRural areaSpotted feverEnvironmental healthFamily medicineImmunologyVeterinary medicineVirologyRickettsiaPathology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.304
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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