Peer-support interventions for women with cardiovascular disease: protocol for synthesising the literature using an evidence map
Bibliographic record
Abstract
INTRODUCTION: The leading cause of death for women is cardiovascular disease (CVD), including ischaemic heart disease, stroke and heart failure. Previous literature suggests peer support interventions improve self-reported recovery, hope and empowerment in other patient populations, but the evidence for peer support interventions in women with CVD is unknown. The aim of this study is to describe peer support interventions for women with CVD using an evidence map. Specific objectives are to: (1) provide an overview of peer support interventions used in women with ischaemic heart disease, stroke and heart failure, (2) identify gaps in primary studies where new or better studies are needed and (3) describe knowledge gaps where complete systematic reviews are required. METHODS AND ANALYSIS: We are building on previous experience and expertise in knowledge synthesis using methods described by the Evidence for Policy and Practice Information (EPPI) and the Coordinating Centre at the Institute of Education. Seven databases will be searched from inception: CINAHL, Embase, MEDLINE, APA PsycINFO, the Cochrane Database of Systematic Reviews and the Cochrane Central Register of Controlled Trials, and Scopus. We will also conduct grey literature searches for registered clinical trials, dissertations and theses, and conference abstracts. Inclusion and exclusion criteria will be kept broad, and studies will be included if they discuss a peer support intervention and include women, independent of the research design. No date or language limits will be applied to the searches. Qualitative findings will be summarised narratively, and quantitative analyses will be performed using R. ETHICS AND DISSEMINATION: The University of Toronto's Research Ethics Board granted approval on 28 April 2022 (Protocol #42608). Bubble plots (ie, weighted scatter plots), geographical heat/choropleth maps and infographics will be used to illustrate peer support intervention elements by category of CVD. Knowledge dissemination will include publication, presentation/public forums and social media.
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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 | 0.131 | 0.181 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.020 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.105 | 0.015 |
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".