Strategies for involving patients and the public in scaling initiatives in health and social services: A scoping review
Bibliographic record
Abstract
BACKGROUND: Scaling in health and social services (HSS) aims to increase the intended impact of proven effective interventions. Patient and public involvement (PPI) is critical for ensuring that scaling beneficiaries' interests are served. We aimed to identify PPI strategies and their characteristics in the science and practice of scaling in HSS. METHODS: In this scoping review, we included any scaling initiative in HSS that used PPI strategies and reported PPI methods and outcomes. We searched electronic databases (e.g., Medline) from inception to 5 February 2024, and grey literature (e.g., Google). Paired reviewers independently selected and extracted eligible reports. A narrative synthesis was performed and we used the PRISMA for Scoping Reviews and the Guidance for Reporting Involvement of Patients and the Public (GRIPP2). FINDINGS: We included 110 unique reports out of 24,579 records. In the past 5 years, the evidence on PPI in scaling has increased faster than in any previous period. We found 236 mutually nonexclusive PPI strategies among 120 scaling initiatives. Twenty-four initiatives did not target a specific country; but most of those that did so (n = 96) occurred in higher-income countries (n = 51). Community-based primary health care was the most frequent level of care (n = 103). Mostly, patients and the public were involved throughout all scaling phases (n = 46) and throughout the continuum of collaboration (n = 45); the most frequently reported ethical lens regarding the rationale for PPI was consequentialist-utilitarian (n = 96). Few papers reported PPI recruitment processes (n = 31) or incentives used (n = 18). PPI strategies occurred mostly in direct care (n = 88). Patient and public education was the PPI strategy most reported (n = 31), followed by population consultations (n = 30). CONCLUSIONS: PPI in scaling is increasing in HSS. Further investigation is needed to better document the PPI experience in scaling and ensure that it occurs in a meaningful and equitable way. PATIENT AND PUBLIC CONTRIBUTION: Two patients were involved in this review. They shared decisions on review questions, data collection instruments, protocol design, and findings dissemination. REVIEW REGISTRATION: Open Science Framework on 19 August 2020 (https://osf.io/zqpx7/).
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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 | 0.187 | 0.377 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.045 | 0.040 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".