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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/). [ABSTRACT FROM AUTHOR]

This article proposes multivariate copula models for hierarchical data. They account for two types of correlation: one is between variables measured on the same unit, and the other is a correlation between units in the same cluster. This model is used to carry out copula regression for hierarchical data that gives cluster‐specific prediction curves. In the simple case where a cluster contains two units and where two variables are measured on each one, the new model is constructed with a D‐vine. The proposed copula density is expressed in terms of three copula families. When the copula families and the marginal distributions are normal, the model is equivalent to a normal linear mixed model with random cluster‐specific intercepts. Methods to select the three copula families and to estimate their parameters are proposed. We perform Monte Carlo studies of the sampling properties of these estimators and of out‐of‐sample predictions. The new model is applied to a dataset on the marks of students in several schools. [ABSTRACT FROM AUTHOR]