MétaCan
Menu
Back to cohort

Migrant communities and the COVID-19 response in sub-Saharan Africa

2020· article· en· W3037406816 on OpenAlexaff
Ikenna Daniel Molobe, Oluwakemi Ololade Odukoya, Brenda Isikekpei, Flavio F. Marsiglia

Bibliographic record

VenuePan African Medical Journal · 2020
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsCentre for Global Health Research
Fundersnot available
KeywordsPreparednessEconomic growthPandemicContext (archaeology)Vulnerability (computing)OutreachDevelopment economicsPublic healthRefugeeCoronavirus disease 2019 (COVID-19)Political scienceMedicineSocioeconomicsGeographyDiseaseSociologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Migration, which involves the movement of people from one location to another, could have a range of social, health, political and economic effects. Migration also has consequences for the individual, the area of origin and the area of destination. The effect of global migration can influence public protection, resulting in health services and environmental issues. Thus, migration, either domestic or transnational, has intense effect on socio-economic development, which could be positive or negative. This article focuses on the impact of the recent global pandemic of the coronavirus disease (COVID-19) on the migrant communities of sub-Saharan region of the African continent. It argues that the distinctive socio-economic development needs and context of the region merit the international community’s undivided attention. The unique challenges of sub-Saharan migrant communities include the high levels of vulnerability faced by the internally displaced migrants during the pandemic. The response to the COVID-19 challenges requires the active outreach and engagement of these communities in the preparedness response. The article concludes with a set of policy and practice recommendations to the sub-Saharan African governments on how to engage these populations on the COVID-19 response to reduce overall morbidity and mortality.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.062
GPT teacher head0.340
Teacher spread0.279 · 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 designNot applicable
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

Citations7
Published2020
Admission routes1
Has abstractyes

Explore more

Same venuePan African Medical JournalSame topicMigration, Health and TraumaFrench-language works237,207