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Background: Active Support, now widely adopted by disability support organizations, is difficult to implement. The study aim was to identify the factors associated with good Active Support.Methods: Data on service user and staff characteristics, quality of Active Support and practice leadership were collected from a sample of services from 14 organizations annually for between 2 and 7 years, using questionnaires, structured observations and interviews. Data were analysed using multilevel modelling (MLM). Results:Predictors of good Active Support were adaptive behaviour, practice leadership, Active Support training, and time since its implementation. Heterogeneity, having more than six people in a service and larger organizations were associated with lower quality of Active Support. Conclusions:In order to ensure that Active Support is consistently implemented, and thus, quality of life outcomes improved, organizations need to pay attention to both service design and support for staff through training and practice leadership. K E Y W O R D S active support, engagement, Implementation, multilevel model, practice leadership, supported accommodation, training [Correction added on 16 November 2019, after first online publication: the affiliation of Shane Kavanagh was incorrect and has been corrected in this current version.] | 335 Published for the British Institute of Learning Disabilities BIGBY et al. Support and changes in residents' depressive symptoms, challenging behaviour, adaptive skills, choice and community participation were reported across studies, Flynn et al.'s (2018) meta-analysis did not demonstrate convergence on the direction or significance of change for any of these factors. The evidence, albeit limited, of an association between Active Support and reduction in challenging behaviour suggests its complementarity to behavioural support strategies. For example, Ockendon, Ashman, and Beadle-Brown (2017) argued that Active Support is a foundational element of Positive Behaviour Support (PBS), setting the context for its successful implementation, and McGill, Ashman, and Beadle-Brown (2014) demonstrated Active Support as an integral component of PBS, which was associated with reductions in challenging behaviour. From a staff perspective, Active Support has been found to be associated with increased staff job satisfaction and a lower propensity for staff to leave their employment (Beadle-Brown, Hutchinson, & Whelton, 2012; Rhodes & Toogood, 2016). | 343Published for the British Institute of Learning Disabilities BIGBY et al.providing coaching to develop nuanced skills in supporting service users with varied ability levels. Strong practice leadership could also maximize the potential of Active Support, as an integral part of PBS, in improving the quality of life of service users with challenging behaviours. | CON CLUS IONSThe contributions of this study emanate from being the largest investigation into Active Support in services in Australia and in evaluating the multilevel nature of factors at individual ...

Public health agencies tasked with improving the health of communities are poorly supported by many 'business-as-usual' funding practices. It is commonplace to call for more funding for health promotion, but additional funding could do more harm than good if, at the same time, we do not critically examine the micro-processes that lead to health enablementmicro-processes that are instigated or amplified by funding. We are currently engaged in a university-and-policy research partnership to identify how funding mechanisms may better serve the practice of community-based health promotion. We propose three primary considerations to inform the way funds are used to enable community-based health promotion. The first is a broader understanding and legitimising of the 'soft infrastructure' or resources required to enhance a community's capacity for change. The second is recognition of social relationships as key to increasing the availability and management of resources within communities. The third consideration understands communities to be complex systems and argues that funding models are needed to support the dynamic evolution of these systems. By neglecting these considerations, current funding practices may inadvertently privilege communities with pre-existing capacity for change, potentially perpetuating inequalities in health. To begin to address these issues, aspects of funding processes (e.g., stability, guidance, evaluation, and feedback requirements) could be designed to better support the flourishing of community practice. Above all, funders must recognise that they are actors in the health system and they, like other actors, should be reflexive and accountable for their actions.

A number of theoretical approaches suggest that gender inequity may give rise to health risks for men. This study undertook a multilevel analysis to ascertain if state-level measures of gender inequity are predictors of men's mortality in the United States. Data for the analysis were taken primarily from the National Longitudinal Mortality Study, which is based on a random sample of the non-institutionalised population. The full data set included 174,703 individuals nested within 50 states and had a six-year follow-up for mortality. Gender inequity was measured by nine variables: higher education, reproductive rights, abortion provider access, elected office, management, business ownership, labour force participation, earnings and relative poverty. Covariates at the individual level were age, income, education, race/ethnicity, marital status and employment status. Covariates at the state level were income inequality and per capita gross domestic product. The results of logistic multilevel modelling showed a number of measures of state-level gender inequity were significantly associated with men's mortality. In all of these cases greater gender inequity was associated with an increased mortality risk. In fully adjusted models for all-age adult men the elected office (OR 1.05 95% CI 1.01–1.09), business ownership (OR 1.04 95% CI 1.01–1.08), earnings (OR 1.04 95% CI 1.01–1.08) and relative poverty (OR 1.07 95% CI 1.03–1.10) measures all showed statistically significant effects for each 1 standard deviation increase in the gender inequity z-score. Similar effects were seen for working-age men. In older men (65+ years) only the earnings and relative poverty measures were statistically significant. This study provides evidence that gender inequity may increase men's health risks. The effect sizes while small are large enough across the range of gender inequity identified to have important population health implications.