Patient, caregiver and other knowledge user engagement in consensus-building healthcare initiatives: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Patient engagement and integrated knowledge translation (iKT) processes improve health outcomes and care experiences through meaningful partnerships in consensus-building initiatives and research. Consensus-building is essential for engaging a diverse group of experienced knowledge users in co-developing and supporting a solution where none readily exists or is less optimal. Patients and caregivers provide invaluable insights for building consensus in decision-making around healthcare, policy and research. However, despite emerging evidence, patient engagement remains sparse within consensus-building initiatives. Specifically, our research has identified a lack of opportunity for youth living with chronic health conditions and their caregivers to participate in developing consensus on indicators/benchmarks for transition into adult care. To bridge this gap and inform our consensus-building approach with youth/caregivers, this scoping review will synthesise the extent of the literature on patient and other knowledge user engagement in consensus-building healthcare initiatives. METHODS AND ANALYSIS: Following the scoping review methodology from Joanna Briggs Institute, published literature will be searched in MEDLINE, EMBASE, CINAHL and PsycINFO databases from inception to July 2023. Grey literature will be hand-searched. Two independent reviewers will determine the eligibility of articles in a two-stage process, with disagreements resolved by a third reviewer. Included studies must be consensus-building studies within the healthcare context that involve patient engagement strategies. Data from eligible studies will be extracted and charted on a standardised form. Abstracted data will be analysed quantitatively and descriptively, according to specific consensus methodologies, and patient engagement models and/or strategies. ETHICS AND DISSEMINATION: Ethics approval is not required for this scoping review protocol. The review process and findings will be shared with and informed by relevant knowledge users. Dissemination of findings will also include peer-reviewed publications and conference presentations. The results will offer new insights for supporting patient engagement in consensus-building healthcare initiatives. PROTOCOL REGISTRATION: https://osf.io/beqjr.
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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 | 0.200 | 0.156 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.027 | 0.020 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.072 | 0.020 |
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".