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Record W4387221913 · doi:10.1186/s13643-023-02347-6

Systematic scoping review protocol of Stroke Patient and Stakeholder Engagement (SPSE)

2023· article· en· W4387221913 on OpenAlexaff
Juliet Roudini, Sarah Weschke, Torsten Rackoll, Ulrich Dirnagl, Gordon Guyatt, Hamid Reza Khankeh

Bibliographic record

VenueSystematic Reviews · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsMcMaster UniversityImpact
FundersKarolinska Institutet
KeywordsMedicineStakeholderStakeholder engagementSystematic reviewProtocol (science)Health careKnowledge managementRehabilitationConceptual frameworkProcess managementNursingMEDLINEPublic relationsAlternative medicineComputer scienceBusinessPolitical science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.015
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.532
GPT teacher head0.518
Teacher spread0.014 · 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 teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreProtocol

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

Citations6
Published2023
Admission routes1
Has abstractyes

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