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Record W4408632803 · doi:10.1017/gmh.2025.29

Women’s household decision-making autonomy and mental health outcomes in Mozambique

2025· article· en· W4408632803 on OpenAlexaff
Roger Antabe, Gregory Antabe, Yujiro Sano, Cornelius K. A. Pienaah

Bibliographic record

VenueCambridge Prisms Global Mental Health · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsWestern UniversityNipissing UniversityThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsAutonomyMental healthPsychologySociologyPolitical sciencePsychiatryLaw

Abstract

fetched live from OpenAlex

Studies point to the role of sociocultural and household power dynamics in women's risk of mental illnesses. Using the context of Mozambique, we examined the association between women's household decision-making autonomy with probable depression and reporting symptoms of anxiety. We used the 2022-2023 Mozambique Demographic and Health Survey and applied logistic regression analysis. Our findings indicate high prevalence rates of depression (10%) and anxiety (11%) among married women. We also find that married women with the highest forms of household autonomy who take decisions alone on their health care (OR = 0.43, 95% CI = 0.32, 0.59; OR = 0.52, 95% CI = 0.38, 0.70), on making large household purchases (OR = 0.43, 95% CI = 0.28, 0.64; OR = 0.52, 95% CI = 0.35, 0.76) and visiting family members or relatives (OR = 0.36, 95% CI = 0.25, 0.51; OR = 0.64, 95% CI = 0.46, 0.89) were all less likely to report propable depression and symptoms of anxiety, respectively. Additionally, higher household wealth and employment acted as protective assets against both depression and anxiety. We recommend working to remove the sociocultural barriers to women's autonomy while improving their socioeconomic status, such as income and employment opportunities, which will lead to a better mental health outcome and serve as an important pathway to increasing their autonomy.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.331
Teacher spread0.318 · 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.

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

Citations10
Published2025
Admission routes1
Has abstractyes

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