What factors influence organisational readiness for change? Implementation of the Australian clinical pathway for the screening, assessment and management of anxiety and depression in adult cancer patients (ADAPT CP).

AIMS: Translation of evidence-based psycho-oncology interventions into routine care can significantly improve patient outcomes, yet effective implementation remains challenging due to numerous real-world barriers. A key factor that may influence implementation is organisational readiness for change. This mixed method study sought to identify factors associated with organisational readiness for implementing the Australian clinical pathway for the screening, assessment and management of anxiety and depression in adult cancer patients (ADAPT CP). METHODS: We collected data from multidisciplinary staff across six Australian cancer services who were preparing to implement the ADAPT CP. Services were categorised as having 'high' versus 'mid-range' organisational readiness based on a median split on the Organizational Readiness for Implementing Change (ORIC) questionnaire (score range = 12-60). Qualitative data from the semi-structured interviews based on the Promoting Action Research in Health Services (PARiHS) framework were analysed thematically and compared for services with high- versus mid-range organisational readiness. RESULTS: Three services with high- (mean ORIC range, 52.25-56.88), and three with mid-range (range, 38.75-46.39) organisational readiness scores were identified. Staff at services reporting higher readiness described a more collaborative and proactive service culture, strong communication processes and greater role flexibility. They also reported greater confidence in overcoming anticipated barriers and clearer strategies for addressing issues. CONCLUSIONS: Levels of organisational readiness were related to distinct qualitative themes. Targeting these issues in services where readiness is mid-range or low prior to full-scale roll-out may improve staff levels of confidence and efficacy in implementing psycho-oncology-focused interventions.

Characterising group-level brain connectivity: A framework using Bayesian exponential random graph models.

The brain can be modelled as a network with nodes and edges derived from a range of imaging modalities: the nodes correspond to spatially distinct regions and the edges to the interactions between them. Whole-brain connectivity studies typically seek to determine how network properties change with a given categorical phenotype such as age-group, disease condition or mental state. To do so reliably, it is necessary to determine the features of the connectivity structure that are common across a group of brain scans. Given the complex interdependencies inherent in network data, this is not a straightforward task. Some studies construct a group-representative network (GRN), ignoring individual differences, while other studies analyse networks for each individual independently, ignoring information that is shared across individuals. We propose a Bayesian framework based on exponential random graph models (ERGM) extended to multiple networks to characterise the distribution of an entire population of networks. Using resting-state fMRI data from the Cam-CAN project, a study on healthy ageing, we demonstrate how our method can be used to characterise and compare the brain's functional connectivity structure across a group of young individuals and a group of old individuals.

Assessing dynamic functional connectivity in heterogeneous samples.

Several methods have been developed to measure dynamic functional connectivity (dFC) in fMRI data. These methods are often based on a sliding-window analysis, which aims to capture how the brain's functional organization varies over the course of a scan. The aim of many studies is to compare dFC across groups, such as younger versus older people. However, spurious group differences in measured dFC may be caused by other sources of heterogeneity between people. For example, the shape of the haemodynamic response function (HRF) and levels of measurement noise have been found to vary with age. We use a generic simulation framework for fMRI data to investigate the effect of such heterogeneity on estimates of dFC. Our findings show that, despite no differences in true dFC, individual differences in measured dFC can result from other (non-dynamic) features of the data, such as differences in neural autocorrelation, HRF shape, connectivity strength and measurement noise. We also find that common dFC methods such as k-means and multilayer modularity approaches can detect spurious group differences in dynamic connectivity due to inappropriate setting of their hyperparameters. fMRI studies therefore need to consider alternative sources of heterogeneity across individuals before concluding differences in dFC.