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Record W2059120605 · doi:10.1186/1471-2458-12-1084

Socio-demographic factors influencing knowledge, attitude and practice (KAP) regarding malaria in Bangladesh

2012· article· en· W2059120605 on OpenAlexfundno aff
Kabirul Bashar, Hasan Mohammad Al‐Amin, Md Selim Reza, Muzahidul Islam, Asaduzzaman, Touhid Uddin Ahmed

Bibliographic record

VenueBMC Public Health · 2012
Typearticle
Languageen
FieldMedicine
TopicMalaria Research and Control
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceBangabandhu Sheikh Mujibur Rahman Agricultural UniversityPublic Health AgencyPublic Health Agency of Canada
KeywordsMalariaBiostatisticsResidenceEnvironmental healthObservational studyPublic healthMedicineEpidemiologySocioeconomicsCross-sectional studyInterviewGeographyDemographyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: A clear understanding of the social and behavioral risk factors, and knowledge gaps, related to exposure to malaria are essential when developing guidelines and recommendations for more effective disease prevention in many malaria endemic areas of the world including Bangladesh and elsewhere in the South East Asia. To-date, the level of knowledge that human populations, residing in moderate to high malaria risk zones, have with respect to the basic pathogen transmission dynamics, risk factors for malaria or disease preventative strategies, has not been assessed in Bangladesh. The purpose of this study was to address this gap by conducting surveys of the knowledge, attitudes and practices (KAP) of people, from variable socio-demographic backgrounds, residing in selected rural malaria endemic areas in Bangladesh. METHODS: The KAP survey was conducted in portions of six different malaria endemic districts in Bangladesh from July to October 2011. The survey consisted of interviewing residence of these malaria endemic districts using a structured questionnaire and interviewers also completed observational checklists at each household where people were interviewed. The study area was further divided into two zones (1 and 2) based on differences in the physical geography and level of malaria endemicity in the two zones. Data from the questionnaires and observational checklists were analysised using Statistical Package for Social Sciences 16.0 (SPSS, Inc., Chicago, IL, USA). RESULTS: A total of 468 individuals from individual households were interviewed, and most respondents were female. Monthly incomes varied within and among the zones. It was found that 46.4% and 41% of respondents' family had malaria within the past one year in zones 1 and 2, respectively. Nearly 86% of the respondents did not know the exact cause of malaria or the role of Anopheles mosquitoes in the pathogen's transmission. Knowledge on malaria transmission and symptoms of the respondents of zones 1 and 2 were significantly (p<0.01) different. The majority of respondents from both zones believed that bed nets were the main protective measure against malaria, but a significant relationship was not found between the use of bed net and prevalence of malaria. A significant relationship (p<0.05) between level of education with malaria prevalence was found in zone 1. There was a positive correlation between the number of family members and the prevalence of malaria. Houses with walls had a strong positive association with malaria. Approximately 50% of the households of zones 1 and 2 maintained that they suffered from malaria within the last year. A significant association (p<0.01) between malaria and the possession of domestic animals in their houses was found in both zones. People who spent time outside in the evening were more likely to contract malaria than those who did not. CONCLUSION: To address the shortcomings in local knowledge about malaria, health personnel working in malaria endemic areas should be trained to give more appropriate counseling in an effort to change certain deeply entrenched traditional behaviors such as spending time outdoors in the evening, improper use of bed nets and irregular use of insecticides during sleep.

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)0.006
metaresearch head score (Gemma)0.006
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 score0.089
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.069
GPT teacher head0.372
Teacher spread0.303 · 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

Citations40
Published2012
Admission routes1
Has abstractyes

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