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Record W2796307035 · doi:10.1186/s12936-018-2291-8

Local knowledge and practices towards malaria in an irrigated farming community in Ghana

2018· article· en· W2796307035 on OpenAlexaff
Hector Attu, Jones Adjei

Bibliographic record

VenueMalaria Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicMalaria Research and Control
Canadian institutionsRed Deer Polytechnic
Fundersnot available
KeywordsMalariaEnvironmental healthMedicinePsychological interventionPublic healthAgricultureHealth careRural areaGeographyNursingImmunologyEconomic growthPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Although malaria is endemic across Ghana, the risk is generally elevated for residents living in and around stagnant water bodies such as dams and irrigated farming projects. What knowledge do these at-risk populations have about the aetiology and symptoms of malaria? What are their coping strategies? And what interventions are needed to help improve the health outcomes of people living in high-risk malaria communities? METHODS: This study addressed these research questions with primary data, comprising both qualitative interviews and quantitative surveys, collected in Asutsuare-a rural irrigated farming community located in the Greater Accra Region of Ghana. RESULTS: Results from the fieldwork showed that awareness of malaria as a major health concern in the community was universal. Respondents also displayed a high knowledge of some common clinical symptoms of malaria. Yet, only 3% out of the total survey respondents of 337 indicated they immediately visit a health facility for treatment whenever they suspected malaria. The overwhelming majority (about 97%) indicated they only visit a healthcare facility for treatment if they felt the suspected malaria illness was severe and/or other treatment options had failed. CONCLUSION: Malaria testing training for drug dispensing personnel as well as the provision of malaria testing kits in drug dispensing stores are necessary to facilitate early malaria screening and timely diagnosis particularly in rural endemic areas.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.376
Teacher spread0.321 · 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.

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

Citations32
Published2018
Admission routes1
Has abstractyes

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