Patient and public involvement (PPI) in health care: A systematic mapping review of systematic reviews
Bibliographic record
Abstract
Introduction: While patient and public involvement (PPI) is increasingly required by policymakers and funding agencies, the vast amount of literature on this topic makes it difficult for stakeholders who are at the beginning of their journey to find guidance on conceptual frameworks, successful methods, and good examples for PPI in contexts/settings similar to their own. Although there are various conceptual frameworks available, especially for PPI in research [1], little is known about which conceptual frameworks are successfully being implemented in health care practice. Recently, [2] highlighted different theoretical traditions and the need to further develop theory-driven approaches to guide the process of involvement in more strategic ways. Objectives: The overarching goal of this mapping review is to identify systematic reviews on PPI in health care practice worldwide to: further examine the different conceptualizations of PPI in health care used in the literature (i.e., definitions, models, frameworks); provide an overview of the methods used to promote and implement PPI in health care; provide an overview of contextual factors such as: - the settings in which PPI is implemented in health care; - the demographic characteristics of the patients and the publics involved; - the desired, achieved and reported outcomes of PPI; and - the outcomes used to measure the effectiveness of PPI. Method and preliminary results: Methodologically, a mapping review design was chosen to give an overview of findings, identify research gaps in the field and to provide the audience with intuitive and accessible visualisations. This review is co-designed and carried out in partnership with a patient (JB), who is also an active volunteer, representative and chair of a patient organization. In December 2021, the study was registered on the Open Science Forum (https://osf.io/9b5j3) and an initial search on PubMed, Cinahl and PsycInfo resulted in 3763 articles after deduplication. Titles and abstracts were screened independently by 2 reviewers (VC, JB) and 95 articles were selected for full-text review. 35 articles were included in the study for detailed data extraction (VC, IB, JB). The themes covered in the articles range from PPI in the education and skills training of health care staff, PPI in design, development and evaluation of health care services and products, quality improvement, patient safety, infectious disease control, crisis management, mental health and health promotion. While data extraction has been completed, data synthesis is still ongoing at the time of abstract submission. An updated search is planned for December 2022. With this mapping review of systematic reviews, we hope to provide an overview of the field and some guidance to those wanting to involve patients, caregivers and the public in their upcoming projects. References [1] Greenhalgh T, et al. Frameworks for supporting patient and public involvement in research: Systematic review and co-design pilot. Health Expectations 2019;22(4):785-801. DOI: 10.1111/hex.12888. [2] Ocloo J, et al. Exploring the theory, barriers and enablers for patient and public involvement across health, social care and patient safety: a systematic review of reviews. Health Research Policy and Systems 2021;19(1):8. DOI: 10.1186/s12961-020-00644-3.
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 | 0.089 | 0.272 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.041 | 0.042 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".