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Record W3159627818 · doi:10.1186/s12905-021-01327-z

Factors influencing healthy menopause among immigrant women: a scoping review

2021· review· en· W3159627818 on OpenAlexafffund
Ping Zou, Thumri Waliwitiya, Yan Luo, Winnie Sun, Jing Shao, Hui Zhang, Yanjin Huang

Bibliographic record

VenueBMC Women s Health · 2021
Typereview
Languageen
FieldMedicine
TopicMenopause: Health Impacts and Treatments
Canadian institutionsOntario Tech UniversityUniversity of British ColumbiaNipissing University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCINAHLPsychosocialPsycINFOAcculturationMedicineSocial supportGerontologyPsychological interventionImmigrationMEDLINENursingPsychologySocial psychologyPsychiatryPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Many factors influence the menopausal transition and the complexity of this transition increases with the addition of immigration transition. This review aims to identify the factors that influence the menopausal transition for immigrant women based on ecosocial theory. METHODS: A scoping review of English publications was conducted according to PRISMA guidelines using CINAHL, AgeLine, MEDLINE, PsycINFO, ERIC, Nursing and Allied Health Database, PsycARTICLES, Sociology Database, and Education Research Complete. Thirty-seven papers were included for this review. RESULTS: The factors which influence the menopausal transition for immigrant women were grouped into three categories: (a) personal factors, (b) familial factors, and (c) community and societal factors. Personal factors include income and employment, physical and psychological health, perceptions of menopause, and acculturation. Familial factors include partner support, relationships with children, and balancing family, work, and personal duties. Community and societal factors encompassed social network, social support, healthcare services, traditional cultural expectations, and discrimination in host countries. CONCLUSIONS: Interventions addressing the menopausal transition for immigrant women should be designed considering different psychosocial factors and actively work to address systemic barriers that negatively impact their transition.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.375
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0120.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.137
GPT teacher head0.432
Teacher spread0.295 · 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; both teacher heads agree on what is shown here.

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

Citations28
Published2021
Admission routes2
Has abstractyes

Explore more

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