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Record W4401454476 · doi:10.3390/women4030021

Women’s Empowerment and Mental Health: A Scoping Review

2024· review· en· W4401454476 on OpenAlexaffabout
Nilanga Aki Bandara, Shams M. F. Al-Anzi, Angelina Zhdanova, Saima Hirani

Bibliographic record

VenueWomen · 2024
Typereview
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEmpowermentMental healthPsychologyPolitical sciencePsychotherapist

Abstract

fetched live from OpenAlex

Women have unique experiences with mental health challenges that require relevant strategies and interventions that effectively support their mental health. Empowerment interventions that vary in nature and format have the potential to play a key role in supporting women’s mental health. The purpose of this scoping review is to outline empowerment interventions targeting improvement in the mental health of women living in Canada.A search was undertaken using major databases including Medline, Cumulative Index for Nursing and Allied Health Literature (CINAHL), PsycINFO, and the Cochrane Library for studies published between 2013 and 2023. A total of 243 articles were identified, from which 12 were ultimately included in this review. All included studies were conducted in Canada but were diverse in design, setting, and sample size. A total of four types of interventions were identified including mental health and emotional awareness, reading, peer support, and skill building and engagement. The findings of the review inform key insights for mental health care and service providers to focus on sustainable outcomes for women’s mental health. The findings also guide the need for a systematic review to appraise the existing empowerment interventions for women’s mental health outcomes.

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)0.008
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 score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0120.015
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.002
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 head0.042
GPT teacher head0.397
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 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

Citations6
Published2024
Admission routes2
Has abstractyes

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