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Record W3026719402 · doi:10.3968/11622

COVID-19, Culture and Public Health Conditions in Developing Countries: Prevention Is Better Than Cure

2020· article· en· W3026719402 on OpenAlexvenueno aff
Kenneth Ubani

Bibliographic record

VenueCanadian social science · 2020
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsnot available
Fundersnot available
KeywordsSustenancePublic healthWorryPublic relationsOutbreakEconomic growthMedicinePolitical scienceLawNursingVirology

Abstract

fetched live from OpenAlex

The virus that is spreading worldwide today has made every society to be cautious about their public health system. Death reports and infection from the virus come in everyday from many parts of the world. But a signal is obvious about the outbreak of this disease which points to modern food culture. The alert focuses on sub-Saharan Africa as of today, given that it did not appear for the first time in any these countries. The worry is that these societies may not have adequate facilities to contain the scourge. The nature of the society and their response to the outbreak, first of all depends on the reality of information communication method. The reaction often exposes the culture and characteristics of the people’s perception, quality of education and development. Public health concern should be the utmost in these regions by the international health institutions to help prevent its spread in such regions. It started from an environment to spread. The concern points to public health conditions. Health institutions and medical experts have provided approaches to detection, symptoms and treatment .As at the time of this writing, no cure has been determined scientifically. Thus this expose encourages sustenance through preventive measures coupled with suggestions that have already been stated by WHO and other world health experts with the conclusion that modern culture can be guaranteed or determined by scientific screening.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.747
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.079
GPT teacher head0.398
Teacher spread0.319 · 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 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

Citations4
Published2020
Admission routes1
Has abstractyes

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