Patient, family and caregiver engagement in diabetes care: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: New treatments and technologies have advanced diabetes care; however, diabetes continues to have a major impact on the daily lives of affected individuals, especially among equity-deserving groups. Evidence from patient engagement literature suggests that involving diverse patients in healthcare can create cost-effective improvements and enhanced efficiency in care that has high patient acceptability and numerous health benefits, as well as improved provider satisfaction. A scoping review will be conducted to provide a cohesive and comprehensive understanding of patient engagement practices and the resulting outcomes. METHODS AND ANALYSIS: The review will follow the recommendations for the conduct of scoping reviews developed by the Joanna Briggs Institute (JBI) Scoping Review Methodology Group. The review will include English-language literature published between 1 January 1990 and the present, searched through MEDLINE (Ovid), Embase (Ovid), CINAHL (EBSCOhost), PsycINFO (Ovid), International Bibliography of the Social Sciences (IBSS), Sociological Abstracts, Applied Social Sciences Index and Abstracts (ASSIA), Scopus, Social Sciences Citation Index and Campbell Collaboration; hand searches; and grey literature. Literature that describes conceptualisations of engagement, methods/strategies for engagement and/or evaluations of engagement across different levels of diabetes care, including direct care, organisational design and governance and policymaking will be included. The review will encompass quantitative, qualitative and mixed-methods studies. Research that is secondary, published in languages other than English, or not specifically focused on patient engagement will be excluded. Screening and extraction will be completed by two independent reviewers and conflicts will be resolved by discussion or a third reviewer, with piloting at each step. Studies will be analysed through descriptive numerical summary and content analysis. ETHICS AND DISSEMINATION: No ethical or safety considerations are pertinent to this work. The results will be disseminated to patients/patient advocacy groups, diabetes organisations, clinicians, researchers, decision-makers and policymakers by way of summary documents, infographics, meeting presentations and through peer-reviewed publications. TRIAL REGISTRATION NUMBER: The protocol has been registered with Open Science Framework: https://doi.org/10.17605/OSF.IO/KCD7Z.
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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 | 0.096 | 0.065 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.080 | 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".