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Record W2154384861 · doi:10.1177/1474515114529690

The influence of ethnicity and gender on navigating an acute coronary syndrome event

2014· article· en· W2154384861 on OpenAlexaff
Kathryn King‐Shier, Shaminder Singh, Pamela LeBlanc, Charles Mather, Rebecca Humphrey, Hude Quan, Nadia Khan

Bibliographic record

VenueEuropean Journal of Cardiovascular Nursing · 2014
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsSt. Paul's HospitalUniversity of British ColumbiaUniversity of Calgary
Fundersnot available
KeywordsEthnic groupMedicineAcute coronary syndromeEthnic chineseCohortPsychiatryMyocardial infarctionInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Ethnicity and gender may influence acute coronary syndrome patients recognizing symptoms and making the decision to seek care. OBJECTIVE: To examine these potential differences in European (Caucasian), Chinese and South Asian acute coronary syndrome patients. METHODS: In-depth interviews were conducted with 20 European (Caucasian: 10 men/10 women), 18 Chinese (10 men/eight women) and 19 South Asian (10 men/nine women) participants who were purposively sampled from those participating in a large cohort study focused on acute coronary syndrome. Analysis of transcribed interviews was undertaken using constant comparative methods. RESULTS: Participants followed the process of: having symptoms; waiting/denying; justifying; disclosing/ discovering; acquiescing; taking action. The core category was 'navigating the experience'. Certain elements of this process were in the forefront, depending on participants' ethnicity and/or gender. For example, concerns regarding language barriers and being a burden to others varied by ethnicity. Women's tendency to feel responsibility to their home and family negatively impacted the timeliness in their decisions to seek care. Men tended to disclose their symptoms to receive help, whereas women often waited for their symptoms to be discovered by others. Finally, the thinking that symptoms were 'not-urgent' or something over which they had no control and concern regarding potential costs to others were more prominent for Chinese and South Asian participants. CONCLUSION: Ethnic- and gender-based differences suggest that education and support, regarding navigation of acute coronary syndrome and access to care, be specifically targeted to ethnic communities.

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)0.006
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.947
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.029
GPT teacher head0.323
Teacher spread0.293 · 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 designObservational
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

Citations23
Published2014
Admission routes1
Has abstractyes

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