Modelling the impact on a local mental health system of previously implemented care programs:The experience of assertive outreach teams in Bizkaia (Spain)

Aims The study assessed the interactions and the impact of specialist mobile community care teams (assertive outreach teams or AOTs) implemented in the mental health (MH) system of Bizkaia (Spain) using a methodology derived from an ecosystem perspective. Methods First, the experts assessed the system's services and codified them according to an international classification system. Second, following an iterative methodology for expert-knowledge elicitation, a clients' flow diagram showing the inter-dependencies of the system's components was developed. It included variables and their relationships represented in a causal model. Third, the system elements where the AOTs had a major impact (stress nodes) were identified. Fourth, three scenarios (variable combinations representing the 'stress points' of the system) were modelled to assess its relative technical efficiency (technical performance indicator). Results The classification system identified the lack of fidelity of the AOTs to the original assertive community treatment model, categorizing them as non-acute low-intensity mobile care. The causal model identified the following elements of the system as 'stress nodes' in relation to AOT: users' families; social services (outside of the healthcare system); acute hospitals; non-acute residential facilities and, to a lesser extent, acute hospital day care services. When the stress nodes inside the healthcare system were modelled separately, acute and non-acute hospital care services resulted in a large deterioration in the system performance, while acute day hospital care had only a small impact. Conclusions The development of the expert-knowledge-based causal model from an ecosystem perspective was helpful in combining information from different levels, from nano to macro, to identify the components in the system likely to be most affected by a potential policy intervention, such as the closure of AOTs. It was also able to illustrate the interaction between the MH system components over time and the impact of the potential changes on the technical performance of the system. Such approaches have potential future application in assisting with service planning and decision-making in other health systems and socio-economic contexts.</p

Albo’s ‘Blight on the Hill’ is blinding the MSM

Jacinta Nampijinpa Price says she wants to ‘make Australia great again’. The reaction? ‘They finally said the quiet bit out loud.’Peter Dutton’s son has to save to buy a house and he’s struggling to do so? Dutton is a millionaire, so he ‘is either lying or cruel’.The mainstream media (MSM) is so bored they are looking for anything to pick up on except the reality of our economic situation. Albanese is leading us to his ‘blight on the hill’. It’s not a beacon of social justice; it’s a place where the Greens finally leverage Labor to use the rest of other peoples’ money to implement their socialist ideas.Make no mistake, we are deep in it. If the Coalition do not get over the line on May 3, then we only have ourselves to blame

Multidisciplinary Allied Health Reablement Model of Care for Older People in Residential Aged Care and Community Settings:Mixed-Methods Evaluation

This study aimed to explore the feasibility, acceptability, and clinical outcomes of a best practice-aligned multidisciplinary allied health reablement model of care for older people. A mixed-methods pre-post-intervention study was conducted in two nursing homes and the community. Quantitative measures were collected for frailty, physical function, and quality of life for all participants pre-implementation and 12 weeks post-implementation. Semi-structured interviews were conducted with a sub-group of participants and allied health professionals involved in the intervention. Participants' ( n = 50) physical function increased (SPPB 4.2 vs. 4.9) while frailty (FRAIL-NH 6.0 vs. 5.5) and quality of life (16 vs. 16) were maintained. There was a high retention (93%) and attendance rate (84%), indicating acceptability. Participants received a daily median of 16 allied health minutes, costing $26AUD. Findings confirm acceptability and feasibility of the model with potential to maintain or improve clinical outcomes. Future work is needed to define long-term outcomes, scalability and sustainability. The study was registered with the ANZCTR [Trial ID: ACTRN12623000915651; Registration Date: 12/1/2024]. </p

Where next for global climate deliberation:From proof of concept to a role in transformation

Building on the proof of concept that was the 2021 Global Assembly on the Climate and Ecological Crisis, we explore an expanded and even institutionalized role for effective and consequential citizen deliberation in global climate governance. Such an expanded role could not just strengthen citizen voices in negotiations, but also counter vested interests, promote civic learning, enhance the legitimacy of governance, foster global solidarity, and generate reflective input to galvanize policies.</p

Asthma medication usage after environmental exposure to wildfire smoke:A systematic review

Asthma is a chronic respiratory condition exacerbated by exposure to particulate air pollution. Smoke from landscape fires has been associated with increased mortality, asthma-related admissions to emergency and other hospital departments, and uptake in primary care services. With climate change and more frequent landscape fires, healthcare systems must prepare for disaster, including surges in asthma medication demand. Past reviews have not resolved the direction and magnitude of the association between PM 2.5 exposure during landscape fires and asthma medication use. The aim of this review was to investigate the relationship between exposure to landscape fire smoke and the use of asthma medications. We conducted a systematic review of PubMed, Scopus, and Web of Science, identifying peer-reviewed articles that examined asthma medication usage following environmental exposure to landscape fire smoke. After a full-text review, we identified twelve articles, three from Canada, three from the USA and six from Australia, with five being retrospective cohort studies. Despite differences in study design, outcome and exposure assessment, the included studies reported a consistent increase in asthma medication use after exposure to wildfires. There is consistent evidence that exposure to wildfire smoke is associated with an increase in the use of reliever medications, particularly salbutamol. Increases in other asthma management medications were also consistently identified. Increases in demand for asthma medications after exposure to wildfire smoke highlight the urgent need to address the growing frequency and intensity of wildfires driven by climate change. </p

Coronavirus research topics, tracking twenty years of research

Research publications aimed at understanding the various aspects of Coronaviruses, particularly COVID-19, have significantly shaped our knowledge base. While the urgency to monitor COVID-19 in real-time has decreased, the continual influx of new research of monthly articles underscores the importance of systematic review and analysis to deepen our understanding of the pandemic’s broad impact. To explore research trends and innovations in this space, we developed a pipeline using natural language processing techniques. This pipeline systematically catalogues and synthesises the vast array of research articles, leading to the creation of a dataset with more than eight hundred thousand articles from July 2002 to May 2024. This paper describes the content of this dataset and provides the necessary information to make this dataset accessible and reusable for future research. Our approach aggregates and organises global research related to Coronaviruses into thematic clusters such as vaccine development, public health strategies, infection mechanisms, mental health issues, and economic consequences. Also, we have leveraged the contribution of health experts to review and revise the dataset.</p

Novel Clinical Assessment of Visual, Vestibular, Somatosensory, and Autonomic Function:Establishing Test Re-Test Reliability in a Healthy Population

Introduction: Maintaining balance is a complex process involving the integration of information from the visual, vestibular, and somatosensory (VVS) systems, along with autonomic nervous system (ANS) function. Traditional assessments of these systems are often expensive, limited to specialised settings or focus on overall balance outcomes, potentially overlooking deficits in the individual systems. This study aimed to evaluate the test-retest reliability of a novel, fully portable clinical assessment tool designed to provide objective measures for individual components of the VVS system and ANS function. Methods: Twenty-eight participants (aged 20–88 years), with no comorbidities and meeting Australian physical activity guidelines, completed the protocol twice. The novel clinical assessment tool comprised of two systems: (1) a virtual reality-type headset incorporating eye-tracking to evaluate visual-vestibular function (smooth pursuit and voluntary saccades), and autonomic function (pupil light reflex); and (2) the Active Movement Extent Discrimination Apparatus (AMEDA) for somatosensory function. Reliability was assessed using two-way mixed-effects model (consistency type, single rater) Intraclass Correlation Coefficients (ICC 3,1) calculated in R-studio. The standard error of measurement (SEM) and minimal detectable change (MDC) was also calculated. Bland-Altman plots were utilised to visualise the agreement between two test repeats. Results: Each metric demonstrated at least moderate to good test re-test reliability: left and right AMEDA (ICC = 0.69 and 0.75), smooth pursuit (ICC = 0.67), voluntary saccades (ICC = 0.53), autonomic response delay (ICC = 0.80), parasympathetic function (ICC = 0.86), and sympathetic function (ICC = 0.89). Discussion: This study supports the reliability of a new, fully portable clinical assessment tool to assess VVS and ANS function. By demonstrating the reliability of this new streamlined tool for evaluating the VVS and ANS systems, the findings of this study has the potential to enhance clinical practice and research in falls prevention and balance rehabilitation.</p

Diagnostic analytics for the mixed Poisson INGARCH model with applications

In statistical diagnosis and sensitivity analysis, the local influence method plays a crucial role and is sometimes more advantageous than other methods. The mixed Poisson integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) model is built on a flexible family of mixed Poisson distributions. It not only encompasses the negative binomial INGARCH model but also allows for the introduction of the Poisson-inverse Gaussian INGARCH model and the Poisson generalized hyperbolic secant INGARCH model. This paper applies the local influence analysis method to count time series data within the framework of the mixed Poisson INGARCH model. For parameter estimation, the Expectation-Maximization algorithm is utilized. In the context of local influence analysis, two global influence methods (generalized Cook distance and Q-distance) and four perturbations–case weights perturbation, data perturbation, additive perturbation, and scale perturbation–are considered to identify influential points. Finally, the feasibility and effectiveness of the proposed methods are demonstrated through simulations and analysis of a real data set.</p

Explainable Human-centered Traits from Head Motion and Facial Expression Dynamics

We explore the efficacy of multimodal behavioral cues for explainable prediction of personality and interview-specific traits. We utilize elementary head-motion units named kinemes, atomic facial movements termed action units and speech features to estimate these human-centered traits. Empirical results confirm that kinemes and action units enable discovery of multiple trait-specific behaviors while also enabling explainability in support of the predictions. For fusing cues, we explore decision and feature-level fusion, and an additive attention-based fusion strategy which quantifies the relative importance of the three modalities for trait prediction. Examining various long-short term memory (LSTM) architectures for classification and regression on the MIT Interview and First Impressions Candidate Screening (FICS) datasets, we note that: (1) Multimodal approaches outperform unimodal counterparts, achieving the highest PCC of 0.98 for Excited-Friendly traits in MIT and 0.57 for Extraversion in FICS; (2) Efficient trait predictions and plausible explanations are achieved with both unimodal and multimodal approaches, and (3) Following the thin-slice approach, effective trait prediction is achieved even from two-second behavioral snippets. Our implementation code is available at: https://github.com/deepsurbhi8/Explainable_Human_Traits_ Prediction.</p