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Record W2943159204 · doi:10.3968/10890

The Impact of Disaster on the Reproductive Health of Women and Girls in Nigeria

2019· article· en· W2943159204 on OpenAlexvenueno aff
Veronica Akwenabuaye Undelikwo, Michael Ibanbeteliehe Ihwo

Bibliographic record

VenueCanadian social science · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsnot available
Fundersnot available
KeywordsReproductive healthSanitationNeglectEnvironmental healthSexual violenceNatural disasterSocioeconomicsEconomic growthCriminologyPolitical scienceMedicineBusinessPopulationPsychologyGeographySociologyPsychiatryEconomics

Abstract

fetched live from OpenAlex

The last two decades has witnessed an increase frequency and severity of both natural and man-made disasters in Nigeria. Women and girls are more affected by the impact of disasters, which due to their prior poor economic and social status limit their survival skills. The response to disaster in affected communities in Nigeria put more premiums on issues like shelter, food, water and sanitation, human security with less attention on reproductive health and social issues. Disaster and displacement expose women to sexual violence, exploitation, trafficking and abuse, leading to higher rates of unintended pregnancies, risky abortions, and sexually transmitted infections (STIs) as well as other latent issues. This paper assesses the impact of inaction and neglect of reproductive health and other social issues in disaster management. It is our conclusion that the emergency situation provides a possibility and opportunity to enhance knowledge and provide sexual and reproductive health services. Working with traditional authorities, local and national partners can facilitate the implementation of sexual and reproductive health services that also deal with related cultural norms and practices.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.401
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.048
GPT teacher head0.430
Teacher spread0.382 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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