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Record W4283826235 · doi:10.52872/001c.34646

Prioritizing the mental health needs of pregnant adolescents in sub-Saharan Africa

2022· article· en· W4283826235 on OpenAlexaff
Ejemai Eboreime, Adaobi Ezeokoli, Keturah Adams, Aduragbemi Banke‐Thomas

Bibliographic record

VenueJournal of Global Health Neurology and Psychiatry · 2022
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMental healthPsychological interventionSociocultural evolutionPregnancyPublic healthStressorPsychiatryStigma (botany)PsychologyMedicineSocial stigmaEnvironmental healthPolitical scienceNursingFamily medicine

Abstract

fetched live from OpenAlex

Sub-Saharan Africa has the highest rate of adolescent pregnancy in the world, with an estimated prevalence of 19.3%. Whereas adolescent pregnancy is considered on the policy agenda as a public health challenge in many sub-Saharan African countries, the mental health impact, although dire, has not received commensurate attention in the policy space. This is not unconnected with sociocultural norms and stigma associated, not just with mental health, but with teenage pregnancy as well. Similarly, adult maternal mental health, though often relegated, has been receiving increasing attention. But pregnant teenagers are often not the focus of available mental health interventions, even though they are more vulnerable to the same pathophysiological stressors, as well as being uniquely exposed to extreme sociocultural and economic stressors. In this viewpoint, we argue that prioritizing the mental health of adolescent mothers is critical in sub-Saharan Africa. We also make important recommendations to ensure that pregnant adolescents receive the mental health services and support they need.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.040
GPT teacher head0.395
Teacher spread0.356 · 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 designNot applicable
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

Citations9
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Global Health Neurology and PsychiatrySame topicAdolescent Sexual and Reproductive HealthFrench-language works237,207