051 An overview on how patients and the public are involved in scaling initiatives in health and social services: perspectives from a scoping review
Bibliographic record
Abstract
Introduction Patient and public involvement (PPI) is critical for scaling shared decision making. We aim to provide an overview on how patients and the public are involved in scaling initiatives in health and social services. Methods In a scoping review, we included any scaling initiative in health and social services that used PPI strategies and reported any outcome. We searched databases from inception to September 2020, and grey literature. Paired reviewers selected and extracted eligible records. We performed a narrative synthesis. We used the Preferred Reporting Items for Scoping Reviews and the Guidance for Reporting Involvement of Patients and the Public. Results We included 77 unique reports that reported 87 scaling initiatives. Most initiatives that targeted a country occurred in higher-income countries (n = 29). Most scaling initiatives involved patients and the public throughout all phases of the scaling (n = 39). Most PPI was at the level of collaboration (n = 36); most frequently reported ethical lenses for PPI were consequentialist-utilitarian (aiming to increase effectiveness) (n = 68). Discussion Patients and the public are increasingly involved in all phases of scaling. Although collaboration was frequently reported, the practice of co-production does not seem to be well- established yet in the context of scaling. Also, there is a need for more available evidence about how key scaling participants in low- and middle-income countries perceive and communicate about PPI in scaling using their own local concepts, terminology, and knowledge. Finally, a combination of ethical lenses is required in approaching PPI, fostering an understanding of PPI not only as an instrument for increasing effectiveness but also as a duty, a right, and a guiding principle. Conclusions PPI in scaling is increasing in health and social care services but co-production is still a challenge. Co-construction requires extra resources that should be anticipated for scaling.
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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 | 0.068 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.029 | 0.033 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".