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Record W2594984480 · doi:10.1111/tmi.12863

Knowledge and practice regarding dengue and chikungunya: a cross‐sectional study among Healthcare workers and community in Northern Tanzania

2017· article· en· W2594984480 on OpenAlexafffund
Debora C. Kajeguka, Rachelle E. Desrochers, Rose Mwangi, Maseke R. Mgabo, Michael Alifrangis, Reginald A. Kavishe, Franklin W. Mosha, Manisha A. Kulkarni

Bibliographic record

VenueTropical Medicine & International Health · 2017
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsOttawa Public HealthUniversity of OttawaHealth Canada
FundersDanish International Development AgencyGrand Challenges Canada
KeywordsTanzaniaChikungunyaCross-sectional studyDengue feverEnvironmental healthHealth careMedicineGeographySocioeconomicsVirologyPolitical scienceSociology

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate knowledge and prevention practices regarding dengue and chikungunya amongst community members, as well as knowledge, treatment and diagnostic practices among healthcare workers. METHOD: We conducted a cross-sectional survey with 125 community members and 125 healthcare workers from 13 health facilities in six villages in the Hai district of Tanzania. A knowledge score was generated based on participant responses to a structured questionnaire, with a score of 40 or higher (of 80 and 50 total scores for community members and healthcare workers, respectively) indicating good knowledge. We conducted qualitative survey (n = 40) to further assess knowledge and practice regarding dengue and chikungunya fever. RESULTS: = 4.25, P = 0.03). Only 2.4% (n = 3) of all participants had a good knowledge score for chikungunya. In the qualitative study, community members expressed uncertainty about dengue and chikungunya. Some healthcare workers thought that they were new diseases. CONCLUSION: There is insufficient knowledge regarding dengue and chikungunya fever among community members and healthcare workers. Health promotion activities on these diseases based on Ecological Health Mode components to increase knowledge and improve preventive practices should be developed.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.427
Teacher spread0.375 · 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

Citations30
Published2017
Admission routes2
Has abstractyes

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