PERCEIVED BARRIERS TO SECONDARY PREVENTION OF PATIENTS FROM LOW SOCIO ECONOMIC GROUPS
Bibliographic record
Abstract
Background and Aims: People of lower socio-economic status (SES) with coronary heart disease (CHD) are a highly vulnerable population because they have more risk factors for CHD, develop CHD younger, and are less likely to benefit from secondary prevention care and prevention programs. The aim of this qualitative study was to examine factors perceived by key health professionals to effect patients' of lower SES willingness and capacity to reduce cardiovascular risk and benefit from services. Method: Qualitative research; 24 key health professional informants (11 physicians, 11 nurses, 2 other) who work with patients of low SES with CHD in Alberta, Canada were interviewed in a semi-structured format. Results: The participants viewed SES as one of six key heath determinants for people with CHD. Of eight primary factors linked to SES (low income, ethnicity, aboriginal, education, mental illness, older age, single parent, high risk lifestyle), low income was seen to be the most important element. The low income disadvantage was magnified for the homeless, working poor, elderly, aboriginal, mentally ill, rural, and recent immigrants. Main barriers to successful secondary prevention in patients of low SES were perceived to be: the high cost of recommended medication, food, and activity; limited access to information, education and transportation; difficulty arranging and cost of taking time off work; co-morbidities and limited mobility; aboriginal - historical disadvantage, diabetes, and exposure to poverty and violence; homeless and mental illness - survival issues, lack of connection and support; immigrants - language and unfamiliarity magnifying access issues; multiple burdens; hopelessness; and stigma. Conclusions: In patients of lower SES in Canada, a complex web of factors is perceived to curtail willingness and capacity to reduce cardiovascular risk and access to services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".