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Record W4384131346 · doi:10.3390/bs13070581

Schizophrenia: A Review of Social Risk Factors That Affect Women

2023· review· en· W4384131346 on OpenAlexaff
Alexandre González-Rodríguez, M. Natividad, Mary V. Seeman, Jennipher Paola Paolini, A. Balagué, E. Román, Eduard Izquierdo, Anabel Pérez, Anna Vallet, Mireia Salvador, José Antonio Monreal

Bibliographic record

VenueBehavioral Sciences · 2023
Typereview
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAffect (linguistics)Schizophrenia (object-oriented programming)PsychologyPsychological interventionPsychiatryClinical psychologySocial supportDevelopmental psychologyMedicineSocial psychology

Abstract

fetched live from OpenAlex

Social risk factors are long-term or repeated environmental exposures in childhood and youth that change the brain and may, via epigenetic effects, change gene expression. They thus have the power to initiate or aggravate mental disorders. Because these effects can be mediated via hormonal or immune/inflammatory pathways that differ between men and women, their influence is often sex-specific. The goal of this narrative review is to explore the literature on social risk factors as they affect women with schizophrenia. We searched the PubMed and Scopus databases from 2000 to May 2023 using terms referring to the various social determinants of health in conjunction with "women" and with "schizophrenia". A total of 57 studies fulfilled the inclusion criteria. In the domains of childhood and adult abuse or trauma, victimization, stigma, housing, and socioeconomics, women with schizophrenia showed greater probability than their male peers of suffering negative consequences. Interventions targeting appropriate housing, income support, social and parenting support, protection from abuse, violence, and mothering-directed stigma have, to different degrees, yielded success in reducing stress levels and alleviating the many burdens of schizophrenia in women.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.338
GPT teacher head0.539
Teacher spread0.201 · 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 designOther design
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

Citations14
Published2023
Admission routes1
Has abstractyes

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