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OBJECTIVE: To enhance Making Every Contact Count (MECC, an opportunistic approach to health promotion), training in the Third and Social Economy (TSE, all groups and organisations primarily working towards social justice, outside of the government or household) by examining the degree to which the behavioural content of MECC training tackled significant factors influencing MECC delivery. METHODS AND MEASURES: A strategic behavioural analysis design. Semi-structured interviews with service providers (n = 15) and users (n = 5) were coded for barriers and facilitators of MECC delivery using the Theoretical Domains Framework (TDF). Existing MECC training was coded for behaviour change techniques (BCTs) and intervention functions (IFs). The degree to which BCTs and IFs addressed the key TDF domains of influences on MECC delivery in the TSE were examined using prespecified tools. RESULTS: Seven key TDF domains of influences in MECC delivery were identified. Overall, only 9/31 linked BCTs were utilised within MECC training, with percentage utilisation of relevant BCTs for each domain ranging from 0% to 66.7%. Training adequately addressed 2/7 key domains. CONCLUSION: The TSE and healthcare share many common key TDF domains, although there are differences in how each are relevant. Limitations and recommendations for MECC training are discussed.
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Volunteering provides unique benefits to organisations, recipients, and potentially the volunteers themselves. This umbrella review examined the benefits of volunteering and their potential moderators. Eleven databases were searched for systematic reviews on the social, mental, physical, or general health benefits of volunteering, published up to July 2022. AMSTAR 2 was used to assess quality and overlap of included primary studies was calculated. Twenty-eight reviews were included; participants were mainly older adults based in the USA. Although overlap between reviews was low, quality was generally poor. Benefits were found in all three domains, with reduced mortality and increased functioning exerting the largest effects. Older age, reflection, religious volunteering, and altruistic motivations increased benefits most consistently. Referral of social prescribing clients to volunteering is recommended. Limitations include the need to align results to research conducted after the COVID-19 pandemic. (PROSPERO registration number: CRD42022349703). Supplementary Information: The online version contains supplementary material available at 10.1007/s11266-023-00573-z.
RESUMO
Recent developments in artificial intelligence (AI) and machine learning raise the possibility of screening and early diagnosis for neurodegenerative diseases, using 3D scans of the retina. The eventual value of such screening will depend not only on scientific metrics such as specificity and sensitivity but, critically, also on public attitudes and uptake. Differential screening rates for various screening programmes in England indicate that multiple factors influence uptake. In this narrative literature review, some of these potential factors are explored in relation to predicting uptake of an early screening tool for neurodegenerative diseases using AI. These include: awareness of the disease, perceived risk, social influence, the use of AI, previous screening experience, socioeconomic status, health literacy, uncontrollable mortality risk, and demographic factors. The review finds the strongest and most consistent predictors to be ethnicity, social influence, the use of AI, and previous screening experience. Furthermore, it is likely that factors also interact to predict the uptake of such a tool. However, further experimental work is needed both to validate these predictions and explore interactions between the significant predictors.