Engaging women with lived experience in tailoring educational resources for cardiovascular rehabilitation
Bibliographic record
Abstract
OBJECTIVE: Women are consistently less likely than men to be referred to, enroll in, attend, or complete cardiac rehabilitation (CR) programs, despite having similar or greater need. This underrepresentation has been linked to multiple factors, including logistical barriers, psychosocial concerns, and educational resources that do not reflect women's specific preferences, learning styles, or lived experiences. This study aimed to engage women with lived experience (WwLE) in collaboratively refining CR educational materials and to identify modifications to improve content, format, and dissemination. METHODS: Guided by the International Association for Public Participation (IAP2) spectrum (ranging from informing to empowering stakeholders) and the Pride in Patient Engagement in Research (PiPER) toolkit (which outlines patient roles, support, and shared decision-making), 11 WwLE participated in five sequential workshops. Each workshop was structured to increase participant involvement in decision-making - moving from providing feedback (Inform/Consult) to co-design (Involve/Collaborate) and final prioritization (Empower). A trained WwLE facilitated the sessions. Engagement was assessed using PEIRS-22 scores, attendance, and satisfaction surveys. Suggested modifications were recorded and categorized. RESULTS: Engagement was high (mean PEIRS-22 score: 87.6 ± 12.8/100.0), with stable attendance during the first four workshops and peak involvement during "Consult." Participation declined in the final "Empower" session due to scheduling conflicts; however, PEIRS-22 scores remained high, indicating meaningful engagement. Five priorities emerged: improving accessibility; integrating storytelling; ensuring diverse representation; using multi-format delivery (text, audio, video); and expanding dissemination through community and digital channels. CONCLUSION: Engagement of WwLE in resource development enhances the relevance and usability of CR educational materials, which may help address women's lower participation rates. PRACTICE IMPLICATIONS: CR programs and policymakers should incorporate patient engagement frameworks to co-develop educational resources, ensure diversity in content, and tailor dissemination strategies. Implementing these approaches may improve women's uptake, adherence, and outcomes in CR.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".