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Record W4224272008 · doi:10.32920/ihtp.v2i1.1625

Women’s empowerment under the lens of global health equity: Literature review of challenges, best practices, and societal impact

2022· article· en· W4224272008 on OpenAlexaffvenue
Walaa Al-Chetachi, Agafya Krivova, Hanaa Badran, Saida Fathima Azam, Yasmine Shalaby, Mithila Orin, Edith Quintanilla, Shafi Bhuiyan

Bibliographic record

VenueInternational Health Trends and Perspectives · 2022
Typearticle
Languageen
FieldMedicine
TopicSex and Gender in Healthcare
Canadian institutionsUniversity of TorontoMcMaster UniversityQueen's UniversityToronto Metropolitan University
FundersUnited Nations Population FundUNICEF
KeywordsEmpowermentPublic relationsCINAHLPolitical sciencePsychological interventionHealth careGlobal healthBest practiceEquity (law)Context (archaeology)Health equityEconomic growthMedicineBusinessNursingEconomics

Abstract

fetched live from OpenAlex

Introduction: Women’s empowerment refers to strengthening the social, economic, and educational powers of women. Numerous global initiatives have been implemented to enable women to make decisions about their health and lives. The purpose of this literature review was to review women’s health barriers, related best practices, and their impacts on women's empowerment in the global context. Methods: We comprehensively searched databases: PubMed, CINAHL, and Google Scholar and key program websites. To expand our review, we used a snowball strategy to scan articles, documents, reports, and research outputs of global programs and best practices to overcome barriers. Also, we screened publications posted on websites of organizations working on women’s empowerment and monitoring the Sustainable Development Goals progress. Results: Findings suggest significant advancements have been made in the status of women's health over the past two decades. However, the COVID-19 pandemic has aggravated health barriers, impeding, and even reversing progress, which has resulted in widening women’s inequity in access to health care services worldwide. Conclusions: Findings suggest that successful and sustainable programs should have a multisectoral, multilevel and multistakeholder, diversified interventions approach with social empowerment, as well as the need for legislative enforcement against discrimination and harmful practices towards 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.846
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.173
GPT teacher head0.500
Teacher spread0.327 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2022
Admission routes2
Has abstractyes

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