Translating Heart Health Knowledge into Action: A Vascular and Risk Reduction Program for Women Aged 35 to 65 Years.
Bibliographic record
Abstract
BACKGROUND: Globally, about 8.6 million women die each year due to cardiovascular disease with cerebral vascular disease being the third leading cause of death in women. The province of New-foundland and Labrador has one of the highest rates of vascular disease in comparison to the rest of Canada. Women in New-foundland and Labrador have higher rates of vascular disease than their female cohorts across Canada. A vascular risk reduction programfor women aged 35 to 65 years was developed and implemented in a rural and an urban setting. PURPOSE: An evaluation of the program was conducted to assess the impact of the program on participants' satisfaction and to assess how women were able to apply acquired knowledge into their everyday lives to improve their vascular health. PROCEDURE: A thematic analysis of qualitative data collected during tvo focus groups (N=19) was completed. FINDINGS: Three core themes were identified that captured the experiences of the women who participated in the program including Solidifying One's Risk, Translating Knowledge into Action, and Making a Change. IMPLICATIONS: Implementation of community-based vascular education programs must consider the context in which the program is delivered, the population's unique needs, and existing resources if they are to be successful in sustaining healthy lifestyle behaviours known to decrease one's riskfor vascular disease.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".