THE PERCEIVED BENEFITS REPORTED BY WOMEN WHO HAVE COMPLETED A CARDIAC REHABILITATION PROGRAM
Bibliographic record
Abstract
In spite of its effectiveness, Cardiac Rehabilitation (CR) is grossly underutilized, particularly among women. While researchers have demonstrated similar benefits for both men and women, physicians are far less likely to refer their female patients. Issues around access, a lack of physician endorsement, and a host of logistic obstacles all contribute to the under-representation of women in CR programs today. The pervasiveness of CVD and a lack of awareness among women and their primary care physicians regarding risk assessment, further perpetuate the under use of this essential service. Given the gap in understanding women's experience with CR, the purpose of this study was to use ‘interpretive description’ to explore the experiences of female participants in a clinical setting, and to provide a conceptual description of the perceived benefits of CR. Four main themes were identified through a process of content analysis aimed at describing and interpreting the perceived benefits reported. These included 1) perceived benefits. 2) The impact of perceived benefits on the lives of women. 3) Facilitators that enhance perceived benefits, and 4) Barriers to the program. Within these themes it was identified that the women found the benefits of fellowship and camaraderie helpful in meeting their emotional and social needs. A perception of safety during exercise along with skills acquisition motivated the female participants to initiate behavioural change. The overall impact of these benefits led to priority shifts, renewed confidence, and a sense of (re)-gained control. Finally, flexibility within the program, support and encouragement from staff, and the inclusion of family members all enhanced the experience of CR for women. Although the program has a lot to offer female participants, logistic, economic, domestic and psychosocial barriers threaten these benefits. Collectively shifting a focus from a streamlined curriculum to a program that considers the specific needs of each participant, may improve both attendance and completion rates. CR should strive to incorporate more strategies to improve psychosocial health, which may ultimately lead to improved adherence to lifestyle change. Being sensitive to gender differences and learning more about what women want from CR is the first step in creating a program that is feasible, sustainable and beneficial.
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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 | 0.012 |
| 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 | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".