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Record W4399678655 · doi:10.1093/eurjpc/zwae175.189

Evidence-informed development of women-focused cardiac rehabilitation education

2024· article· en· W4399678655 on OpenAlexaffabout
Gabriela L. M. Ghisi, A A Hebert, Paul Oh, Tracey J. F. Colella, Crystal Aultman, Celso B. Carvalho, Rajiv I. Nijhawan, M. K. Ross, Sherry L. Grace

Bibliographic record

VenueEuropean Journal of Preventive Cardiology · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsYork UniversityCentre intégré de santé et de services sociaux de Chaudière-AppalachesToronto Rehabilitation InstituteUniversity Health Network
Fundersnot available
KeywordsMedicineStakeholderCurriculumFocus groupContext (archaeology)Medical educationPatient educationHealth careRehabilitationNursingFamily medicinePublic relationsPhysical therapyPedagogy

Abstract

fetched live from OpenAlex

Abstract Background Cardiovascular rehabilitation (CR) is an outpatient model of secondary preventive care proven to mitigate the burden of cardiovascular disease (CVD). Despite their differential risk factor burden, context and often different forms of heart disease, CR programs generally do not provide women with needed secondary prevention information specific to them. Purpose to co-design evidence-informed, theoretically-based women-focused education for the secondary prevention of CVD. Broadly, we aim to build capacity in women-focused CR education for patients who identify as women and their care partners, multi-disciplinary healthcare professionals and trainees involved in delivery of CR, as well as decision-makers. Methods A multi-disciplinary, multi-stakeholder steering committee (N=18) oversaw the four-phase development of the women-focused curriculum, called ‘Cardiac College for Women’. Phase 1 involved a literature review on women’s CR information needs and preferences, phase 2 a CR program needs assessment, phase 3 content development (including determining content and mode, assigning experts to create the content, plain language review and translation), and phase 4 will comprise evaluation and implementation. In phase 2, a focus group was conducted with Canadian CR providers; it was analyzed using Braun and Clarke’s iterative approach. Results Nineteen providers participated in the focus group, with four themes emerging: current status of education (most CR programs tended not to have specific education for women, or an organizational culture to educate women differently than men), challenges to delivering women-focused education (cost, time, human resources, internet access / barriers to dissemination of education resources, lack of staff training on women’s health and lack of support from their institution), delivery modes and topical resources (women want a multi-modal interactive approach, which incorporates time for motivational discussions along with the education). Results were consistent with those from our related global survey, supporting saturation of themes. Co-designed educational materials included 19 videos. These were organized across 5 webpages in English and French, specific to tests and treatments, exercise, diet, psychosocial well-being, and self-management. Twelve corresponding session slide decks with notes for clinicians were created, to support program delivery in CR flexibly. Conclusion Cardiac College for Women is a multi-modal, accessible, co-designed, evidence-based, plain language, theoretically-informed educational curriculum with resources available in all areas of CVD secondary prevention. It can be delivered based on women’s preferences for modalities and content, as well as CR program realities (e.g., staff, dose). While further evaluation is underway to confirm utility and effectiveness, it is hoped these resources will support women to reduce their risk of cardiovascular sequelae.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.083
metaresearch head score (Gemma)0.071
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.441

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0830.071
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0040.010
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.034
GPT teacher head0.362
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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