MétaCan
Menu
Back to cohort
Record W4382400820 · doi:10.1016/j.heliyon.2023.e17425

Ebola and slum dwellers: Community engagement and epidemic response strategies in urban Sierra Leone

2023· article· en· W4382400820 on OpenAlexafffund
Zuzana Hrdličková, Joseph Macarthy, Abu Conteh, S. Harris Ali, Victoria Blango, A. Sesay

Bibliographic record

VenueHeliyon · 2023
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsYorkville UniversityYork University
FundersInternational Development Research CentreYork University
KeywordsSierra leoneSlumCommunity engagementGeographySocioeconomicsEnvironmental healthEconomic growthPolitical scienceMedicineSociologyPublic relationsPopulation

Abstract

fetched live from OpenAlex

The Ebola epidemic in West Africa (2013-2016) was a learning process for all - the population, health experts and practitioners, as well as government structures. Learning occurred in all stages of the response, from the initial lack of clarity and denial of Ebola's existence that contributed to public confusion; to the eventual acceptance of the existence of the Ebola threat whereupon fear and stigmatization reigned; to the later stages in which community engagement and ownership of the response arose. In this paper we describe how two urban poor communities in informal settlements in the Western Area of Sierra Leone responded to Ebola Virus Disease and how they deployed efficient strategies like the development and implementation of by-laws for monitoring and surveillance, thus helping to curb the epidemic. For future public health emergencies, we recommend that community engagement be pursued earlier and that efforts are made to ensure two-way knowledge exchange between responders and community stakeholders.

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)0.002
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 score0.679
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.110
GPT teacher head0.374
Teacher spread0.263 · 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

Citations9
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueHeliyonSame topicViral Infections and Outbreaks ResearchFrench-language works237,207