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Background: There is a workforce shortage in the children's hospice sector, but there has been little research on the specific challenges of working in this setting and on how these challenges might be alleviated. To identify appropriate interventions to improve staff wellbeing, the drivers of wellbeing in children's hospices need to be known and measured. This paper reports on the development of two measures, one for work-related rewards and one for work-related stressors, for use in children's hospice care teams. Methods: A mixed-methods, four-stage study; the first three phases focused on the development of the scales, and the last stage focused on the validation of the scales. Participants of all stages were children's hospice care team staff members in the UK. Stage 1: survey assessing the relevance and comprehensiveness of the original scale items (N = 60); Stages 2 (focus groups; N = 16) and 3 (cognitive interviews; N = 14) to assess content validity; Stage 4: UK-wide survey (N = 414) to validate the final version of the new, children's hospice-specific scales using Rasch Analysis (RA) and Confirmatory Factor Analysis (CFA). Results: Due to poor fitting indices shown in the results from the RA, five items (out of 36) were removed from the new rewards scale used in the UK-wide survey and 20 (out of 62) were removed from the new stressors scale. CFA also supported the removal of the items and showed a one-factor structure for the rewards scale and a three-factor structure for the stressors scale were adequate—the sub-scales for the stressors scale related to caring for an ill or dying child ("Child" sub-scale), working with parents and families ("Parent" sub-scale), and stressors related to organisational factors, such as team conflict and workload ("Organisation" sub-scale). Conclusions: Both of the new scales showed good psychometric properties and can be useful in clinical settings and research to assess the perceived intensity of the work-related rewards and stressors for children's hospice staff. [ABSTRACT FROM AUTHOR]

There is increasing interest in modelling longitudinal dietary data and classifying individuals into subgroups (latent classes) who follow similar trajectories over time. These trajectories could identify population groups and time points amenable to dietary interventions. This paper aimed to provide a comparison and overview of two latent class methods: group-based trajectory modelling (GBTM) and growth mixture modelling (GMM). Data from 2963 mother–child dyads from the longitudinal Southampton Women's Survey were analysed. Continuous diet quality indices (DQI) were derived using principal component analysis from interviewer-administered FFQ collected in mothers pre-pregnancy, at 11- and 34-week gestation, and in offspring at 6 and 12 months and 3, 6–7 and 8–9 years. A forward modelling approach from 1 to 6 classes was used to identify the optimal number of DQI latent classes. Models were assessed using the Akaike and Bayesian information criteria, probability of class assignment, ratio of the odds of correct classification, group membership and entropy. Both methods suggested that five classes were optimal, with a strong correlation (Spearman's = 0·98) between class assignment for the two methods. The dietary trajectories were categorised as stable with horizontal lines and were defined as poor (GMM = 4 % and GBTM = 5 %), poor-medium (23 %, 23 %), medium (39 %, 39 %), medium-better (27 %, 28 %) and best (7 %, 6 %). Both GBTM and GMM are suitable for identifying dietary trajectories. GBTM is recommended as it is computationally less intensive, but results could be confirmed using GMM. The stability of the diet quality trajectories from pre-pregnancy underlines the importance of promotion of dietary improvements from preconception onwards. [ABSTRACT FROM AUTHOR]