MétaCan
Menu
Back to cohort
Record W4225247606 · doi:10.3390/women2010004

Understanding the Influence of Ghanaian Women’s Migration Patterns on Access to Health Care

2022· article· en· W4225247606 on OpenAlexaff
Laiba Rizwan, Michelle Malagón, Solina Richter

Bibliographic record

VenueWomen · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsSocioeconomic statusThematic analysisHealth careEmpowermentIntersectionalityHealth equitySocial determinants of healthEconomic growthQualitative researchPolitical sciencePsychologyDemographic economicsBusinessPopulationSociologyMedicineGender studiesEnvironmental healthEconomics

Abstract

fetched live from OpenAlex

Increased migration rates demonstrate a rise in women seeking relocation as a means to access employment or academic opportunities; this is referred to as the feminization of migration. Migration stimulates female empowerment, increases access to financial opportunities, and promotes cultural diversity, all while simultaneously exposing women to detrimental conditions that impose risks to their physical and psychological well-being. Health is a fundamental human right that female migrants often are deprived of due to various social, cultural, political, and economic factors. A secondary analysis design was implemented to explore the impact of social determinants of health, specifically socioeconomic status, culture, and education, on health outcomes and health care access of Ghanaian internal and external female migrants. Interviews collected from two primary studies were analyzed using thematic analysis and an intersectionality approach. Ghanaian female migrants experienced cultural, financial, social, and health accessibility related barriers in accessing health care services. Our findings will serve as a foundation for improving health outcomes for female migrant populations and support health care professionals’ practice of cultural competence.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.999

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 studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.108
GPT teacher head0.436
Teacher spread0.328 · 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 designQualitative
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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueWomenSame topicGlobal Health Workforce IssuesFrench-language works237,207