To the Editor: Estrogen status appears to modify the risk of developing Alzheimer disease (AD). It has recently been proposed that this may be due to the effect

Motivational interviewing (MI) is internationally recognised as an effective intervention to facilitate health-related behaviour change; although, how it is best implemented and maintained in everyday clinical practice is not so clear. The aim of this study is to understand how MI as an intervention can be embedded and sustained in the clinical practice and learning environments. A concurrent iterative mixed methodology was utilised. Data collection occurred in two parts: a scoping review to identify reported barriers and enablers to embedding and sustaining MI in healthcare settings, and a survey of health professionals at an international clinical educator workshop on the topic. Results from both methods were integrated at the analysis phase (‘following a thread’) to understand how MI is embedded and the fidelity sustained in the clinical environments. Complexity theory as a conceptualising framework was utilised. Eleven studies were included, and 30 health professionals were surveyed. Sustainability of MI at micro-clinical levels can be fostered through use of enabling technology, focus on patient-centred care, personnel development and process improvement. At the meso-organisational level, developing shared vision, creating opportunities and an organisational culture supportive of continuous learning are relevant issues. At the macro levels, adopting systems thinking and a learning organisation approach is important for sustaining MI. In addressing the recognised barriers to embedding and sustaining MI in health service provisions, clinical educators could potentially play a central role as change agents within and across the complex clinical system.

Undergraduate students across health professions are required to be capable users of evidence in their clinical practice after graduation. Gaining the essential knowledge and clinical behaviors for evidence-based practice can be enhanced by theory-based strategies. Limited evidence exists on the effect of underpinning undergraduate EBP curricula with a theoretical framework to support EBP competence. A systematic review was conducted to determine the effectiveness of EBP teaching strategies for undergraduate students, with specific focus on efficacy of theory-based strategies. This review critically appraised and synthesized evidence on the effectiveness of EBP theory-based teaching strategies specifically for undergraduate health students on long or short-term change in multiple outcomes, including but not limited to, EBP knowledge and attitudes. PubMed, CINAHL, Scopus, ProQuest Health, ERIC, The Campbell Collaboration, PsycINFO were searched for published studies and The New York Academy of Medicine, ProQuest Dissertations and Mednar were searched for unpublished studies. Two independent reviewers assessed studies using the Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument. Twenty-eight studies reporting EBP teaching strategies were initially selected for review with methodological quality ranging from low to high. Studies varied in course duration, timing of delivery, population and course content. Only five included papers reported alignment with, and detail of, one or more theoretical frameworks. Theories reported included Social Cognitive Theory (one study), Roger’s Diffusion of Innovation Theory (two studies) and Cognitive Apprenticeship Theory (one study). Cognitive Flexibility Theory and Cognitive Load Theory were discussed in two separate papers by the same authors. All but one study measured EBP knowledge. Mixed results were reported on EBP knowledge, attitudes and skills across the five studies. EBP programs for undergraduate health students require consideration of multiple domains, including clinical behaviors, attitudes and cognitive learning processes; Interventions grounded in theory were found to have a small but positive effect on EBP attitudes. The most effective theory for developing and supporting EBP capability is not able to be determined by this review therefore additional rigorous research is required.

Objective: This scoping review aims to map the roles of rural and remote primary health care professionals (PHCPs) during disasters.Introduction: Disasters can have catastrophic impacts on society and are broadly classified into natural events, man-made incidents, or a mixture of both. The PHCPs working in rural and remote communities face additional challenges when dealing with disasters and have significant roles during the Prevention, Preparedness, Response, and Recovery (PPRR) stages of disaster management.Methods: A Johanna Briggs Institute (JBI) scoping review methodology was utilized, and the search was conducted over seven electronic databases according to a priori protocol.Results: Forty-one papers were included and sixty-one roles were identified across the four stages of disaster management. The majority of disasters described within the literature were natural events and pandemics. Before a disaster occurs, PHCPs can build individual resilience through education. As recognized and respected leaders within their community, PHCPs are invaluable in assisting with disaster preparedness through being involved in organizations’ planning policies and contributing to natural disaster and pandemic surveillance. Key roles during the response stage include accommodating patient surge, triage, maintaining the health of the remaining population, instituting infection control, and ensuring a team-based approach to mental health care during the disaster. In the aftermath and recovery stage, rural and remote PHCPs provide long-term follow up, assisting patients in accessing post-disaster support including delivery of mental health care.Conclusion: Rural and remote PHCPs play significant roles within their community throughout the continuum of disaster management. As a consequence of their flexible scope of practice, PHCPs are well-placed to be involved during all stages of disaster, from building of community resilience and contributing to early alert of pandemics, to participating in the direct response when a disaster occurs and leading the way to recovery.

Background: Opioid Use Disorder (OUD) and opioid overdose (OD) impose huge social and economic burdens on society and health care systems. Research suggests that Medication for Opioid Use Disorder (MOUD) is effective in the treatment of OUD. We use machine learning to investigate the association between patient’s adherence to prescribed MOUD along with other risk factors in patients diagnosed with OUD and potential OD following the treatment. Methods: We used longitudinal Medicaid claims for two selected US states to subset a total of 26,685 patients with OUD diagnosis and appropriate Medicaid coverage between 2015 and 2018. We considered patient age, sex, region level socio-economic data, past comorbidities, MOUD prescription type and other selected prescribed medications along with the Proportion of Days Covered (PDC) as a proxy for adherence to MOUD as predictive variables for our model, and overdose events as the dependent variable. We applied four different machine learning classifiers and compared their performance, focusing on the importance and effect of PDC as a variable. We also calculated results based on risk stratification, where our models separate high risk individuals from low risk, to assess usefulness in clinical decision-making. Results: Among the selected classifiers, the XGBoost classifier has the highest AUC (0.77) closely followed by the Logistic Regression (LR). The LR has the best stratification result: patients in the top 10% of risk scores account for 35.37% of overdose events over the next 12 month observation period. PDC score calculated over the treatment window is one of the most important features, with better PDC lowering risk of OD, as expected. In terms of risk stratification results, of the 35.37% of overdose events that the predictive model could detect within the top 10% of risk scores, 72.3% of these cases were non-adherent in terms of their medication (PDC <0.8). Targeting the top 10% outcome of the predictive model could decrease the total number of OD events by 10.4%. Conclusions: The best performing models allow identification of, and focus on, those at high risk of opioid overdose. With MOUD being included for the first time as a factor of interest, and being identified as a significant factor, outreach activities related to MOUD can be targeted at those at highest risk.