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Record W2989412830 · doi:10.5539/gjhs.v11n14p37

An Evaluation of the Impact of Media Campaign Against Female Genital Mutilation (FGM) in the Rural Communities of Enugu State, Nigeria

2019· article· en· W2989412830 on OpenAlexvenueno aff
Joseph Oluchukwu Wogu, Chinenye Amonyeze, Raphael Oluwasina Babatola Folorunsho, Henry Egi Aloh

Bibliographic record

VenueGlobal Journal of Health Science · 2019
Typearticle
Languageen
FieldMedicine
TopicFemale Genital Mutilation/Cutting Issues
Canadian institutionsnot available
Fundersnot available
KeywordsFemale circumcisionIgboModernization theoryMedicineEnvironmental healthSocioeconomicsMass mediaFamily medicineGeographyDemographySociologyPolitical scienceGynecologyAdvertisingBusiness

Abstract

fetched live from OpenAlex

This paper investigates the impact of media campaign against Female Genital Mutilation (FGM) in the rural areas of Enugu State. One hundred and sixty three women attending the antenatal clinics in six rural communities and twenty-four heads of Women groups were selected as sample for this cross-sectional survey. Structured questionnaire and interview were used to collect data while analysis of the data was done with SPSS version 20.0. The results reveal among others that the media campaign against FGM in Enugu state is ineffective. It further reveals that cultural values (51%), poor network reception (23%), epileptic power supply (18%), inaccessible media, and the nature of the content of the campaign are responsible for the ineffectiveness. Given the findings, the researchers recommends the modernization of the media and the contents of its FGM campaign for rural reach/accessibility, the development of pro-Igbo cultural programmes against FGM practice, and the use of visual methods to establish/prove the relationship between FGM, infections/diseases and maternal deaths. Further empirical research on FGM – maternal health care in Enugu State is recommended.

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)0.008
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.049
GPT teacher head0.396
Teacher spread0.346 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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