Data analysis
REGRESSION
Descriptive statistics in the form of means, frequencies, and proportions were used to summarise service and staff characteristics, and the ORIC and additional study-specific survey item responses.To determine how the ORIC items would be grouped and used in the analyses, the latent structure of the ORIC was assessed in an exploratory factor analysis. Factors were extracted if they had an eigenvalue > 1 and item loadings were identified using a varimax rotation (with Kaiser normalisation). For ease of interpretability of the rotated factor loadings, an item was considered to load onto a factor if it loaded at least 0.40, and was less than 0.40 for the other factors [Based on the literature and an understanding of the services implementing the ADAPT CP, a number of staff- and service-level factors were identified as having potential to influence staff perceptions of their service’s readiness to implement the ADAPT CP. The staff-level factors identified were: age, gender, role, employment status, length of time in current role, and each survey respondents’ For regression analyses, a minimum of 10 participants per predictor is required to produce reliable results [With the aim of developing a model with predictors that provide the best explanation for the outcome variable, a backward elimination regression was conducted. The initial, full model contained all significant predictors from the univariate regression analyses with variables eliminated from the model, starting with the predictor having the highest non-significant p-value (i.e., The assumptions of multiple regression were also checked. The REDCap survey data was extracted using Microsoft Excel. All analyses were performed using Stata, version 17.0.