Facilitators of and barriers to patient and public involvement in building learning health systems in community health services settings: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: The development of learning health systems (LHSs) has often focused on optimally leveraging data. More attention should be paid to patient and public involvement or community engagement in forming learning communities that work together to build LHS. This scoping review aims to identify facilitators of and barriers to involving patients and the public in building LHSs in community health services settings. METHODS AND ANALYSIS: We will use the Joanna Briggs Institute's scoping review methodology. We will review literature in English published from 1 January 2007 to 31 December 2022. The databases that will be searched are MEDLINE, CINAHL, Embase, Web of Science, Scopus, AgeLine, PsycINFO and Web of Science. Key inclusion and exclusion criteria include the following: we will only consider a learning community in a community health services context (eg, home care, long-term care, primary care); we will exclude literature on acute care settings; and we will consider any research designs apart from big data analytics. We will review all sources, including university student theses and dissertations. The review will proceed in three steps: (1) we will identify keywords and index terms from the MEDLINE and CINAHL databases; (2) using the keywords and index terms identified in step (1), we will search other databases and (3) we will handsearch the reference lists of the selected literature and will search for grey literature using Google. Two research assistants will screen the titles and abstracts separately, with reference to the inclusion criteria. Two researchers will then assess the full text of selected studies, also in reference to the inclusion criteria. We will present the findings in a charting table and provide a narrative summary. ETHICS AND DISSEMINATION: This work does not require ethics approval because the data for this scoping review are publicly available. The findings will be presented in a journal article and at conferences.
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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 | 0.174 | 0.111 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.050 | 0.013 |
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".