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Record W4308304047 · doi:10.1101/2022.11.03.22281885

Understanding Knowledge, Attitudes and Practices on Ebola Virus Disease: A Multi-Site Mixed Methods Survey on Preparedness in Rwanda

2022· preprint· en· W4308304047 on OpenAlexafffund
Janvier Karuhije, Menelas Nkeshimana, Fathiah Zakham, Benjamin Hewins, Justin Rutayisire, Gustavo Sganzerla Martinez, David J. Kelvin, Pacifique Ndishimye

Bibliographic record

VenuemedRxiv · 2022
Typepreprint
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsDalhousie University
FundersDalhousie UniversityGenome CanadaDalhousie Medical Research FoundationResearch Nova ScotiaUNICEF
KeywordsEbola virusPreparednessFamily medicineMedicineOutbreakFocus groupDiseasePersonal protective equipmentEnvironmental healthInfectious disease (medical specialty)Coronavirus disease 2019 (COVID-19)VirologyPathologyPolitical science

Abstract

fetched live from OpenAlex

Abstract The overall goal of this survey was to understand the Ebola Virus Disease (EVD) - related knowledge, attitudes, and practices (KAP) at individual, inter-personal, institutional, and societal levels in Rwanda. This cross-sectional mixed-methods survey was conducted in five selected districts: Rusizi, Karongi, Rubavu, Burera and Gasabo. Quantitative data was collected from 1,010 participants using a structured questionnaire and Kobo Collect. Qualitative data was collected from 98 participants through Key Informant Interviews and Focus Group Discussions using a semi structured interview guide. Among the 1,010 surveyed respondents, 56% were male, 70.3% were married, and 50% had primary education. An important finding was the high level of Ebola awareness and knowledge in all the five districts, with 99.6% reporting having previously heard of Ebola, which indicates previous awareness-raising efforts were successful. More than 54% of respondents indicated that Ebola is caused by a virus which originates from wild animal animals (42.1%). Furthermore, fever (85%), bleeding (87.7), and vomiting (40.2%) were cited as the primary signs and symptoms for Ebola. Most of the respondents were knowledgeable regarding prevention measures for Ebola. Despite this, 80% of the survey respondents had not received formal training or health education on Ebola. The majority of respondents (78.2%) reported having a positive attitude towards EVD survivors. Many respondents (90%) believe that the country is at risk of an EVD outbreak and about 87.8% think that they are personally at risk of contracting Ebola. Most respondents reported adopting habits that included avoiding physical contact with the patients and reducing unnecessary movements/travel throughout the Ebola-affected regions. At the community level, participants state that they participate in the sharing of Ebola-related information and reporting suspected cases to relevant authorities. Additionally, many participants know the necessary emergency contact number (114) for assistance and reporting of EVD-related information. Most respondents (97.2%) believed that it is important to be vaccinated to prevent Ebola, and around 93.3% are ready/willing to be vaccinated once the EVD vaccine is available. While the radio is the preferred source for Ebola-related information, the most trusted sources are the ministry of health and governmental institutions, such as the Rwanda Biomedical Centre. Our results show that there was high EVD-related knowledge and awareness among the general population in Rwanda. However, for strong public health awareness, preparedness, and protection, there is a need to implement public sensitization programmes that address EVD-related misconceptions and discriminatory attitudes toward EVD patients.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.323
GPT teacher head0.511
Teacher spread0.188 · 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 source (direct Gemma or distilled Codex), 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
Published2022
Admission routes2
Has abstractyes

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