Stroke patient and stakeholder engagement (SPSE): concepts, definitions, models, implementation strategies, indicators, and frameworks—a systematic scoping review
Bibliographic record
Abstract
BACKGROUND: Involving stroke patients in clinical research through patient engagement aims to ensure that studies are patient-centered, and may help ensure they are feasible, ethical, and credible, ultimately leading to enhanced trust and communication between researchers and the patient community. In this study, we have conducted a scoping review to identify existing evidence and gaps in SPSE. METHODS: The five-step approach outlined by Arksey and O'Malley, in conjunction with the Preferred Reporting Items for Scoping Reviews (PRISMA-ScR) guidelines, provided the structure for this review. To find relevant articles, we searched PubMed, Web of Science, and Embase databases up to February 2024. Additionally, the review team conducted a hand search using Google Scholar, key journals, and references of highly relevant articles. Reviewers screened articles, selecting eligible English-language ones with available full texts, and extracted data from them into a pre-designed table tested by the research team. RESULT: Of the 1002 articles initially identified, 21 proved eligible. Stakeholder engagement primarily occurred during the design phase of studies and within the studies using qualitative methodologies. Although the engagement of stakeholders in the research process is increasing, practice regarding terminology and principles of implementation remains variable. Researchers have recognized the benefits of stakeholder engagement, but have also faced numerous challenges that often arise during the research process. CONCLUSION: The current study identifies stakeholder groups and the benefits and challenges researchers face in implementing their engagement. Given existing challenges and limited specific models or frameworks, it is suggested to explore applied recommendations for stakeholder engagement in future studies, that may enhance stakeholder engagement, overcome obstacles, and unify researchers' understanding of engagement and implementation.
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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 | 0.162 | 0.258 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.034 | 0.032 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".