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Record W2896485424 · doi:10.5539/gjhs.v10n11p19

Demography as a Determinant of Awareness, Knowledge and Attitude of Asaba Youth to Media Advocacy Campaign on Sickle Cell Disorder

2018· article· en· W2896485424 on OpenAlexvenueno aff
Kolade Ajilore, Kevin Onyenankeya, Emmanuel Morka, Mofoluke Akoja, Babafemi Akintayo, Olusegun Ojomo

Bibliographic record

VenueGlobal Journal of Health Science · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)PopulationDiseaseState (computer science)PsychologyMedicineEnvironmental health

Abstract

fetched live from OpenAlex

In a localized attempt to address the inevitable status of Nigeria as the biggest host of patients of sickle cell anaemia, the Delta state government sponsored a media centered initiative to attract public attention to sickle cell disease as well as to the victims of the disorder. This study examined the influence of the media advocacy campaign on youth’s awareness, knowledge and attitude to the disease using the questionnaire survey method involving 300 participants randomly selected from the capital city, Asaba. The results showed that demographic variables such as age, gender, income and religious affiliations had varying influence on awareness, knowledge and attitude of respondents. Although respondents exhibited modest awareness and knowledge of the disorder, it emerged that they arrived at this level of knowledge through information acquired from sources other than the state sponsored media campaign on sickle cell. The study concluded that the media advocacy campaign on sickle cell disorder was yet to resonate with the target population.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.389
Teacher spread0.357 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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