Irene Zeng

Background: Workforce retention poses ongoing challenges for podiatric care provision in Aotearoa New Zealand (NZ); however, intention‐to‐leave (ITL) from the workforce and its associated factors have not been explored. This study aimed to determine the frequency of ITL among NZ podiatrists and identify factors associated with leaving intentions. Methods: A cross‐sectional online survey of NZ‐registered practising podiatrists was conducted. Demographics, employment, workplace and attitudinal measures were collected as independent variables. ITL was assessed using (1) a self‐reported single‐item Likert scale and (2) total scores on the validated six‐item Turnover Intention Scale (TIS‐6), with higher scores indicating greater turnover intention. Associations between both ITL outcomes and independent variables were examined using theory‐informed hierarchical logistic and linear regression models, as appropriate. Results: A total of 143 podiatrist responses were analysed. Over half (50.7%) reported being somewhat or highly likely to leave the profession, with 23.8% indicating plans to exit within 2 years. The mean TIS‐6 total score was 15.41/30 (SD 5.16). In fully adjusted models, workplace and psychosocial factors demonstrated the most consistent associations with ITL. Lower organisational support (OR = 0.60, p = 0.049), lower role satisfaction (OR 0.64, p = 0.041; B = −2.08, p ≤ 0.001), lower perceived professional respect ( B = −1.14, p < 0.05) and higher stress levels (OR 0.57, p = 0.023) were associated with greater ITL. Conclusions: ITL among NZ podiatrists is prevalent, with over half of this sample reporting some ITL and almost one quarter considering a near‐term exit. Given the relatively small NZ podiatry workforce, these findings raise important concerns regarding future workforce sustainability and service‐provision capacity. Addressing modifiable factors such as role satisfaction, organisational support, professional value and workplace stress may represent important targets for retention initiatives.

In the history of biostatistics application in research, researchers typically start with a question or hypothesis and propose a data model. In contrast, recently emerged machine learning methods, often start with the information already available and propose hypothesis using data as the starting point. These represent the "twocultures" paradigms of statistical modelling described by Breiman (2001) as data modelling and algorithmic modelling. The conventional data modelling approach perceives data generated from an underlying probabilistic structure and focuses on estimation, interpretation, and inference. The algorithmic modelling assumes that data may not arise from an easily described specified model. It uses an algorithm to predict the response and is evaluated by measuring predictive accuracy.When the informatics era came, we have a modern problem and the excitement of growing data in terms of its dimensionalities and volume. The growth of informatics has intensified the importance of both modelling approaches. Researchers now routinely work with high-dimensional and multimodal data, including genomics, neuroimaging, wearable sensors, electronic health records, administrative databases, and natural language text. While traditional statistical methods remain critical for causal inference and evaluation in clinical and epidemiological studies, algorithmic approaches provide powerful tools for uncovering complex relationships within increasingly large and interconnected datasets. The two-culture principle suggests that we should search for the model that provides the best solution-data modelling, algorithmic modelling, or both.The current volume of the topic "Advancing Biostatistics and Informatics Applications in Mental health Research" brings together twelve articles of five themes that collectively demonstrate the expanding role of advanced analytics in mental health research.Several studies demonstrate the value of multivariate approaches for characterizing complex mental health phenomena.Chen utilized network structure learning, an algorithmic modelling method, to explore the interrelations between major depression symptoms and ambivalence over emotional expression (AEQ) and compared the symptom networks between male and female college students. They identified two clusters of comorbid symptoms in the depression-AEQ networks, discovered the bridging symptoms between two clusters and examined the gender difference in network and centrality. Notably, the study combined network methods with statistical validation procedures, illustrating productive integration of algorithmic and data modelling approaches.Youvalari and colleagues provided a profiling analysis of resilience and demonstrated the potential of machine learning to enrich psychological Modelling. Grounded in the five-dimensional (5D) resilience framework and used unsupervised algorithmic modelling, the authors identified hidden psychological patterns clustered into 4 archetypes (Reactive Idealists, Fragile Strivers, Hidden Reactors, Stable Withdrawers). An important innovation of the study is to combine a deep autoencoder for feature extraction, with Gaussian Mixture Model clustering; and provided narrative description of archetype profiles.Lee and colleagues proposed digital phenotyping by integrating wearable Fitbit data, SOMDAY application, neuroimaging data (fNIRS), and genomic information. The periodical nature and activities of sleep was used for the multivariate pattern analysis and identification for digital phenotypes. The analytical framework conceptualized integrating the genetic vulnerability (polygenic risk scores), molecular state (gene expression), and physiological outputs (biomarkers) in the context of environmental and behavioral modulating factors captured through digital phenotyping.The New Zealand study by Zeng and colleagues similarly demonstrated the value of analytical triangulation. By combining data modelling using quantile regression, path analysis, with algorithmic Bayesian network modelling within complex survey data, the authors identified consistent associations between mental wellbeing and factors such as perceived health, life satisfaction, cultural identity acceptance, and trust in educational institutions. The study also highlights methodological advances in calibrating complex survey designs through replicate-weight approaches.Advances in natural language processing (NLP) and large language models (LLMs) are increasingly influencing mental health research and clinical practice. Goga and colleagues conducted texts analysis using social media discussions and semantic techniques. A supporting objective of the research was to create a natural language processing (NLP) tool for classifying depression to obtain the corpus necessary for ontology creation. The innovations were automation of ontology generation from unstructured text and compared them across depressed and nondepressed, religious and non-religious groups, in concept, hierarchical and relation similarities.Niederlohmann E. proposed clinical translation methods for classifying psychological session cues using a perceived-assess-dose-safeguard decision matrix and a conflict-square algorithm (PAD-S/CSA model). The study proposed a quantitative translation from less structured narrative clinical diagnosis notes to clear formulated documentation; to assist psychotherapists to provide next treatment move and tolerance threshold for the treatment. It generates a separated process notation to assist clinical reasoning and decision-making based on safety, tolerance, shame, avoidance, and functional recovery for SMI and multimorbidity.Complementing this work, Filos, Regio M.A. presented a decision-analytic evaluation framework in forensic mental health practice, using large language models (LLMs), and decision curve analysis (DCA). Rather than focusing solely on predictive accuracy, the framework emphasizes evidence integrity,...

To explore patients’ experiences and perceptions of physical activity in the context of gout and gout flares. Interpretive description informed the design of this qualitative study. Individual interviews were conducted with 25 people with gout. An interview guide was used to lead discussions, focusing on participants’ experiences of gout flares, physical activity engagement, and the perceived impact of flares on activity levels. Key questions were designed to capture culturally specific understandings and experiences related to gout and physical activity. Interviews were audio-recorded and transcribed verbatim. Data were analysed using reflexive thematic analysis. Four themes were generated from the data: (1) The experience of physical activity is shaped by societal misconceptions about gout; (2) A loss of physicality comes with a loss of autonomy; (3) Reclaiming body and identity through physical activity; and (4) Living with uncertainty: wanting to be active but not knowing how. Gout flares disrupted mobility, independence, work, family roles, and identity. Participants described stigma, fear of judgement, activity modification, pacing and uncertainty about whether exercise could trigger flares or damage joints, particularly in the absence of gout-specific advice. This study offers new insights into how societal misconceptions about gout influence the patient experience of physical activity, including loss of autonomy, identity reconstruction through movement, and uncertainty around safe exercise. These findings may inform future intervention development, and patient-centred physical activity guidance that supports people with gout to move safely, confidently, and meaningfully.

Introduction: In mental health research involved with sensitive features and privacy issues, using integrated data offers an efficient alternative to traditional approaches such as interviews or in−person data collection. These conventional methods frequently face logistical barriers, including low response rates among study populations. Using integrated data from multiple existing administrative and survey sources provides protection for participants and economic savings for researchers, despite being constrained by the limitations of the original data sources. Our research questions were: 1) Can we conduct path and network analyses of school absenteeism, psychosocial factors, and mental health outcomes using the integrated survey data from New Zealand’s Integrated Data Infrastructure (IDI)? and 2) How can we account for the complex design of the General Social Survey (GSS) to ensure representative inference? Materials and methods: The study population was New Zealand youth enrolled in school, aged 15 years and older in 2018, who participated in the 2018 GSS. The study analyzed the Ministry of Education data integrated with the 2018 GSS survey data from New Zealand’s IDI. To explore the relationship between outcomes of including the WHO-5, “perceived life worthwhileness”, “perceived general health”, and predictors including school absenteeism, psychosocial factors, and family factors, quantile mixed−effects regression, path analysis, and Bayesian network (BN) analysis were used. The complex survey design was calibrated using replicated weights from the GSS, a design-based method for complex sampling, in path and regression analyses, and bootstrap resampling, in BN analysis. Results: In descending order from the weighted path model, overall life satisfaction (standardized path coefficient: 0.36), perceived health condition (0.34), ease in accepting cultural identity (0.12), and trust in the education system (0.07) were all significantly (positively) related to students' mental health well-being (WHO-5). Perceived life worthwhileness correlated with the WHO-5 but was only connected to overall life satisfaction. Similar factors were identified for perceived health, with additional attributing factors, such as fear of crime in the area and the family’s overall well-being. Conclusion: The integration of Bayesian networks and path analysis offers rigorous methods for detecting complex interrelationships among integrated mental health outcomes from population data and variables from a complex survey design.

Rationale: Up to 90% of strokes are preventable through the modification and control of lifestyle risk factors. Health and Wellness (HWC) coaching is an established psychological intervention that may address multiple risk factors, including high blood pressure to reduce the risk of stroke. Aims: To determine the effectiveness of HWC in the management of blood pressure and stroke-related modifiable risk factors in reducing the risk of stroke. Methods: This Phase III, open-label, single-blinded, two-arm randomised controlled trial recruited adults with first-ever or recurrent minor stroke or transient ischaemic attack from hospitals in Auckland and Hamilton, New Zealand. Eligible participants were 18 years, independent in activities of daily living, had at least two modifiable cardiovascular risk factors, elevated or treated systolic blood pressure, were English-speaking, and had no history of major stroke, myocardial infarction, significant cognitive or mood disorders, or terminal illness. Longitudinal outcomes will be analysed using linear mixed-effects models under an intention-to-treat framework, with time-to-event outcomes analysed using competing-risk methods and missing data handled using multiple imputation with pooling based on Rubin's rules. Study outcomes: The primary outcome is difference in the mean change from baseline systolic blood pressure (SBP) to 6-months post-randomisation between control (Usual Care, UC) and HWC groups. The study (n=360) is powered 85% (two sided 0.05) to detect a mean difference in change of SBP 6 mm Hg (SD ± 20 mm Hg) between HWC and UC groups at 6-months post-randomisation, accounting for a 20% attrition rate. A revised sample size calculation due to a lower attrition rate (9%) provided a required sample size of 320. Secondary outcomes include cardiovascular health score using the Life's Simple 7; stroke awareness; quality of life; satisfaction with life: cognition; mood; medication adherence; adverse cardiovascular events; health and service costs and productivity status. Discussion: HWC has the potential to modify lifestyle risk factors for stroke. This trial will be the first to test the effectiveness of HWC to modify lifestyle risk factors for secondary stroke prevention

Background: Atrial fibrillation (AF) is a major cause of ischemic stroke (IS), and this risk can be substantially reduced with oral anticoagulants (OACs). Using the population‐based ARCOS (Auckland Regional Community Stroke) study, we investigated the temporal trends in AF‐associated IS. Methods: We analyzed ARCOS IV (March 2011–February 2012) and ARCOS V (September 2020–August 2021). AF‐associated IS incidence was compared using Poisson exact methods. Regional OAC dispensing trends were also evaluated. Propensity score–weighted logistic regression estimated the marginal association between cohort and AF‐associated IS after standardizing for vascular risk factors. Interaction tests were performed for ethnicity, and sex‐specific differences were assessed using heterogeneity testing. OAC‐associated intracerebral hemorrhage rates were also examined. Results: Among 1694 (ARCOS IV) and 1982 (ARCOS V) IS cases, the proportion of AF‐associated IS declined from 32.4% to 21.5% (−10.8% [95% CI −13.7 to −8.0], P p =0.02 for heterogeneity). Temporal reductions differed by ethnicity, with greater decline in the “Other” ethnicity categories, but not in Māori or Pacific peoples. During the same period, patients dispensed OACs increased from 1094 to 1928 per 100 000 ( P <0.001). OAC‐associated intracerebral hemorrhage increased from 2.1 to 3.6 per 100 000, equating to 11 IS cases prevented per excess intracerebral hemorrhage. Conclusion: AF‐associated IS incidence declined in Auckland between 2012 and 2021, during a period of substantially increased community OAC use. Persistent ethnic inequities and sex‐specific variation highlight the need for improved AF detection, risk stratification, and prevention strategies.

In 2021, following the Taliban’s control of Afghanistan, Aotearoa New Zealand (NZ) welcomed over 1,700 Afghan nationals at risk due to their association with NZ agencies. The New Settlers Family and Community Trust (NFACT) was contracted to provide tailored resettlement support. This study evaluated NFACT’s Afghan evacuee resettlement programme using a mixed-methods approach, including a cross-sectional survey, interviews, and focus groups with Afghan evacuees and NFACT staff. Of the 101 surveyed evacuees, most (93.1%) expressed satisfaction with the support they received, and almost all (97.0%) would recommend NFACT’s programme to newcomers. Analysis across integration domains revealed consistently high ratings for services and support. Qualitative findings from Afghan evacuees (n = 12) highlighted the challenges of adjusting to new systems, the crucial guidance provided by NFACT, and the need for ongoing, tailored support to foster their dreams and sense of purpose. NFACT staff (n = 11) emphasised their dedication to support evacuees beyond contractual obligations, how limited resourcing constrained their efforts, and how their shared values and experiences underpinned the success of the programme. The findings underscore the need for comprehensive, culturally sensitive, and sustainable resettlement programmes. These findings have implications for governments to strengthen support for humanitarian evacuation responses within the evolving geopolitical landscape.

Background: Poststroke fatigue affects ≈50% of patients with stroke, causing significant personal, societal, and economic burden. In the FASTER (Fatigue After Stroke Educational Recovery) study, we assessed a group‐based educational intervention for poststroke fatigue. Methods and Results: Two hundred patients with clinically significant fatigue were included and randomized to either a general stroke education control or fatigue management group (FMG) intervention and assessed at baseline, 6 weeks, and 3 months. The FMG involved weekly psychoeducation sessions over 6 weeks. Coprimary outcomes were the Fatigue Severity Scale and Multidimensional Fatigue Inventory‐20 total scores. Adjusted mean total Fatigue Severity Scale scores at 6 weeks (primary end point) were nearly identical for the education control and FMG groups. The adjusted mean difference between treatment groups was −0.13 (SE, 1.4; P =0.92) at 6 weeks and 1.67 (SE, 1.4; P =0.26) at 3 months. Although there were no significant effects, Fatigue Severity Scale outcomes were in the direction of a treatment effect based on the estimated change. Adjusted mean total Multidimensional Fatigue Inventory‐20 scores at 6 weeks (primary end point) were similar for the education control and FMG groups. The adjusted mean difference between treatment groups was −0.91 (SE, 1.54; P =0.55) at 6 weeks and −1.26 (SE, 1.8; P =0.49) at 3 months. Both groups had similar secondary outcomes (eg, Multidimensional Fatigue Inventory‐20 subscales, sleep, pain, mood, quality of life) at 6 weeks and 3 months. Conclusions: We found no evidence of significant group‐level benefits of FMG over and above general stroke education. Educational group‐based interventions for poststroke fatigue should continue to be refined and examined, including consideration of potential impacts at an individual level. Registration: URL: https://www.anzctr.org.au/ ; UnIque identifier: ACTRN12619000626167.