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Record W3026246933 · doi:10.1186/s12978-020-00913-y

Psychosocial interventions targeting mental health in pregnant adolescents and adolescent parents: a systematic review

2020· review· en· W3026246933 on OpenAlexfundno aff
Christina A. Laurenzi, Sarah Gordon, Nina Abrahams, Stefani Du Toit, Melissa Bradshaw, Amanda Brand, G. J. Meléndez‐Torres, Mark Tomlinson, David A. Ross, Chiara Servili, Liliana Carvajal–Aguirre, Joanna Lai, Tarun Dua, Alexandra Fleischmann, Sarah Skeen

Bibliographic record

VenueReproductive Health · 2020
Typereview
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsnot available
FundersEconomic and Social Research CouncilMedical Research CouncilPublic Health AgencyPublic Health Agency of CanadaWorld Health Organization
KeywordsPsychosocialReproductive medicineMental healthPsychological interventionPublic healthMedicinePsychologyPsychiatryPregnancyClinical psychologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Pregnancy and parenthood are known to be high-risk times for mental health. However, less is known about the mental health of pregnant adolescents or adolescent parents. Despite the substantial literature on the risks associated with adolescent pregnancy, there is limited evidence on best practices for preventing poor mental health in this vulnerable group. This systematic review therefore aimed to identify whether psychosocial interventions can effectively promote positive mental health and prevent mental health conditions in pregnant and parenting adolescents. METHODS: We used the standardized systematic review methodology based on the process outlined in the World Health Organization's Handbook for Guidelines Development. This review focused on randomized controlled trials of preventive psychosocial interventions to promote the mental health of pregnant and parenting adolescents, as compared to treatment as usual. We searched PubMed/Medline, PsycINFO, ERIC, EMBASE and ASSIA databases, as well as reference lists of relevant articles, grey literature, and consultation with experts in the field. GRADE was used to assess the quality of evidence. RESULTS: We included 17 eligible studies (n = 3245 participants). Interventions had small to moderate, beneficial effects on positive mental health (SMD = 0.35, very low quality evidence), and moderate beneficial effects on school attendance (SMD = 0.64, high quality evidence). There was limited evidence for the effectiveness of psychosocial interventions on mental health disorders including depression and anxiety, substance use, risky sexual and reproductive health behaviors, adherence to antenatal and postnatal care, and parenting skills. There were no available data for outcomes on self-harm and suicide; aggressive, disruptive, and oppositional behaviors; or exposure to intimate partner violence. Only two studies included adolescent fathers. No studies were based in low- or middle-income countries. CONCLUSION: Despite the encouraging findings in terms of effects on positive mental health and school attendance outcomes, there is a critical evidence gap related to the effectiveness of psychosocial interventions for improving mental health, preventing disorders, self-harm, and other risk behaviors among pregnant and parenting adolescents. There is an urgent need to adapt and design new psychosocial interventions that can be pilot-tested and scaled with pregnant adolescents and adolescent parents and their extended networks, particularly in low-income settings.

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)0.006
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.007
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.073
GPT teacher head0.422
Teacher spread0.349 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations117
Published2020
Admission routes1
Has abstractyes

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