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Record W7117454291 · doi:10.1186/s13104-025-07564-x

Impact of reproductive health intervention on awareness of sexual and reproductive health service among adolescents in the Greater Accra and Eastern Regions of Ghana

2025· article· en· W7117454291 on OpenAlexfundno aff
Rachel G. A. Thompson, Agani Afaya, Isaac Yeboah, Pascal Agbadi, Tricia A. Thompson, Wisdom Agbadi, Jerry John Ouner

Bibliographic record

VenueBMC Research Notes · 2025
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsnot available
FundersGrand Challenges Canada
KeywordsReproductive healthDescriptive statisticsHealth servicesIntervention (counseling)Significant differenceSexually activeSexual behaviorDescriptive research

Abstract

fetched live from OpenAlex

OBJECTIVE: This study used the Knowledge and Access Power (KAP) mobile platform to assess the awareness and knowledge of sexual and reproductive health (SRH) among adolescents in the Greater Accra and Eastern Regions of Ghana. METHODS: A mobile application, referred to as the KAP app was designed to assess SRH awareness and knowledge. To evaluate SRH awareness and knowledge among adolescents, an invitation to download and access the mobile application was sent via social media platforms, including Twitter, Facebook, and WhatsApp. A total of 386 adolescents downloaded the app and attempted the pre- and post-module quizzes. RESULTS: From the quiz participation snapshot data, users attempted 1,040 quizzes. Descriptive statistics revealed that the overall average score received on SRH before completing the learning modules was 67.98 (SD = 26.597), while the overall average score gained on SRH after completing the learning modules was 73.66 (SD = 25.142). The pre- and post-module SRH scores were compared using a paired samples t-test, and the results showed a statistically significant difference between the two sets of scores [t(182) = -2.58, p = 0.010]. Based on these findings, using the KAP app can help increase SRH knowledge among teenagers.

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)0.010
metaresearch head score (Gemma)0.002
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 score0.045
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.369
GPT teacher head0.568
Teacher spread0.199 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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