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Record W6911404526 · doi:10.5281/zenodo.10012793

INFLUENCE OF POVERTY ON TEENAGE PREGNANCY AMONG SCHOOL GOING TEENAGERS IN KOROGOCHO SLUMS, NAIROBI COUNTY, KENYA

2023· article· en· W6911404526 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyTeenage pregnancyNonprobability samplingPopulationStratified samplingDescriptive statisticsSocial classThematic analysisSample (material)

Abstract

fetched live from OpenAlex

Abstract: Teenage pregnancy is becoming a global catastrophe and a global threat. According to the Canadian International Development Agency, the average number of adolescents who become pregnant each year is greater than 16 million, and this number is continually rising. Pregnancy causes young women to drop out of school, which interrupts their education for a period. The situation is not different within Korogocho Slums in Nairobi County. This study assessed the influence of poverty on teenage pregnancy among school going teenagers in Korogocho slums in Nairobi County. The study was informed by Social Disorganization Theory and Intersectionality Theory. The study was guided by a descriptive survey design. The target population of the study involved 5 secondary schools within the slum, 10 social workers, 5 principals, 20 class teachers and 215 students were part of the sample used by the study. Student sample was achieved through stratified random sampling. Purposive sampling was employed for the social workers, class teachers and principals. Quantitative data was analyzed through descriptive statistics by percentages, means and standard deviation. Content and thematic approaches were utilized in analyzing qualitative data. The findings from the study revealed significant associations between poverty and teenage pregnancy. Poverty showed strong positive correlations (r = 0.785, p < 0.05), indicating their substantial impact on teenage pregnancies. The regression analysis further confirmed the influence of poverty on teenage pregnancies, with a positive coefficient of 0.304. These findings highlight the importance of addressing socio-economic disparities to effectively mitigate teenage pregnancies within the community. Accordingly, the study advocates for increased investments in education and healthcare infrastructure, the implementation of comprehensive sex education, and the provision of youth-friendly sexual health services as effective measures to address this issue. It stresses the necessity of collaborative efforts among stakeholders to reduce teenage pregnancies and improve the overall well-being of adolescents. Keywords: Poverty, Teenage, Pregnancy, Slums, School Going, Kenya, Nairobi, Korogocho, Slums. Title: INFLUENCE OF POVERTY ON TEENAGE PREGNANCY AMONG SCHOOL GOING TEENAGERS IN KOROGOCHO SLUMS, NAIROBI COUNTY, KENYA Author: Salma Nanjira Musa Mkanga, Prof David Gichuhi, Dr Alice Omondi International Journal of Social Science and Humanities Research ISSN 2348-3156 (Print), ISSN 2348-3164 (online) Vol. 11, Issue 4, October 2023 - December 2023 Page No: 85-92 Research Publish Journals Website: www.researchpublish.com Published Date: 17-October-2023 DOI: https://doi.org/10.5281/zenodo.10012794 Paper Download Link (Source) https://www.researchpublish.com/papers/influence-of-poverty-on-teenage-pregnancy-among-school-going-teenagers-in-korogocho-slums-nairobi-county-kenya

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)0.002
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 score0.065
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.069
GPT teacher head0.354
Teacher spread0.285 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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