Promoting Immigrant Women's Cardiovascular Health Redesigning Patient Education Interventions
Bibliographic record
Abstract
In Brief Cardiovascular disease is the most common cause of death among women from low- to middle-income countries. The most common cardiovascular nursing intervention is that of patient education. However, the applicability of this intervention is questionable, as these educational initiatives are typically designed and evaluated using samples of “white” homogeneous males. Using the social determinants of health framework, this discursive article identifies specific strategies for redesigning existing cardiovascular education interventions to enhance their applicability to immigrant women. The recommendations will allow nurses to enhance the educational support offered resulting in the reduction and/or prevention of cardiovascular-related symptoms and/or complications. Cardiovascular disease is the most common cause of death among women from low to middle income countries. The most common cardiovascular nursing intervention is that of patient education. However, the applicability of this intervention is questionable, as these educational initiatives are typically designed and evaluated using samples of “white”, homogenous males. Using the social determinants of health framework, this discursive paper identifies specific strategies for redesigning existing cardiovascular education interventions to enhance their applicability to immigrant women.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".