Systematic scoping review protocol of Stroke Patient and Stakeholder Engagement (SPSE)
Bibliographic record
Abstract
This protocol describes a systematic scoping review of Stroke Patient and Stakeholder Engagement (SPSE), concepts, definitions, models, implementation strategies, indicators, or frameworks. The active engagement of patients and other stakeholders is increasingly acknowledged as essential to patient-centered research to answer questions of importance to patients and their caregivers. Stroke is a debilitating, long-lasting burden for individuals, their families, and healthcare professionals. They require rehabilitation services, health care system assistance, and social support. Their difficulties are unique and require the continued involvement of all parties involved. Understanding SPSE in research is fundamental to healthcare planning and extends the role of patients and stakeholders beyond that of the study subject. We will conduct a systematic literature search to identify the types of existing evidence related to SPSE, implementation strategies, indicators, or frameworks related to Patient and Stakeholder Engagement (PSE); clarify key concepts, definitions, and components of SPSE; compile experiences and prerequisites; and identify stroke research internationally. Two independent reviewers will extract data from selected studies onto a customized extraction form that has already been piloted. We integrate existing knowledge to address gaps in the literature on SPSE research by presenting the model, implementation strategies, indicators, and frameworks for stroke patients. We hope that these findings will offer future researchers a clear picture and conceptual model of SPSE.
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 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 | 0.177 | 0.206 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.155 | 0.028 |
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".