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Record W4379376757 · doi:10.7717/peerj.15391

Exposure to mass media family planning messages among men in Nigeria: analysis of the Demographic and Health Survey data

2023· article· en· W4379376757 on OpenAlexaff
Daniel Amoak, Irenius Konkor, Kamaldeen Mohammed, Sulemana Ansumah Saaka, Roger Antabe

Bibliographic record

VenuePeerJ · 2023
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsThe Scarborough HospitalUniversity of TorontoWestern University
Fundersnot available
KeywordsMass mediaSocioeconomic statusLogistic regressionDemographyRural areaEnvironmental healthPsychologySocioeconomicsGeographyMedicineGerontologySociologyPopulationAdvertisingBusiness

Abstract

fetched live from OpenAlex

Background: Family planning (FP) is essential for improving health and achieving reproductive goals. Although men are important participants in FP decision-making within households in Nigeria, a country with one of the highest rates of maternal mortality, we know very little about their exposure to mass media FP messages. Methods: = 13,294), and applying logistic regression analysis, we explored the factors associated with men's exposure to mass media FP messages in Nigeria. Results: A range of socioeconomic, locational, and demographic factors were associated with men's exposure to mass media FP messages. For example, wealthier, more educated, and employed men were more likely to be exposed to mass media FP messages than their poorer, less educated, and unemployed counterparts. In addition, compared to those in rural areas and other regions, men in urban areas as well as South East Region, were more likely to be exposed to mass media FP messages. Finally, younger men and those who belong to the traditional religion were less likely to be exposed to mass media FP messages, compared to their older and Christian counterparts. Conclusions: Based on these findings, we discuss implications and recommendations for policymakers as well as directions for future research.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
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 head0.068
GPT teacher head0.345
Teacher spread0.278 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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