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Record W4402667586 · doi:10.1016/j.ssmqr.2024.100485

Institutional and systemic barriers and facilitators affecting healthcare access for Black women in Alberta

2024· article· en· W4402667586 on OpenAlexafffundabout
Mary Olukotun, Adedoyin Olanlesi-Aliu, Yawa Idi, Tehseen Ladha, Paul Bailey, Regine King, Bukola Salami

Bibliographic record

VenueSSM - Qualitative Research in Health · 2024
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsMental Health Research CanadaUniversity of CalgaryUniversity of Alberta HospitalUniversity of Alberta
FundersWomen and Children's Health Research Institute
KeywordsHealth careNursingMedicineBusinessPolitical science

Abstract

fetched live from OpenAlex

Canada's Black population has experienced significant growth in recent years, with substantial increases noted in the prairie provinces. As Black people continue to make up a growing proportion of the population, it is important to understand their experiences in accessing healthcare services, especially for those who are multiply marginalized. We undertook a qualitative study to examine the healthcare access experiences of Black women in Canada. We completed semi-structured interviews with a sample of 30 Black women from Alberta. Our study was guided by intersectionality to examine how Black women's experience of healthcare access is shaped by social processes related to their socio-demographic characteristics such as being Black, a woman, an immigrant or non-immigrant, and having high or low income. From our thematic analysis we identified three key factors that hinders healthcare access for Black women: socioeconomic barriers, health systems issues, and racism. We identified two types of facilitators: community and institutional facilitators and structural facilitators. Our findings elucidate how Black women's experiences of accessing and utilizing health services in Alberta are influenced by overlapping institutional, structural, and systemic factors.

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)0.022
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.294
GPT teacher head0.630
Teacher spread0.336 · 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 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
Published2024
Admission routes3
Has abstractyes

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