Fire and emergency services personnel face unique occupational hazards, including repeated exposure to potentially psychologically traumatic events (PPTEs), which can impact their mental health. Despite growing awareness, comprehensive data on the mental health of fire and emergency services personnel in New Zealand remains sparse. This study examined the prevalence of symptoms of mental health disorders, including PTSD, Major Depressive Disorder, and Generalized Anxiety Disorder, and assessed the association between PPTE exposure, coping mechanisms, and mental ill health among New Zealand firefighters and commanders. A comprehensive online survey using the Qualtrics platform was administered to fire and emergency personnel, assessing exposure to PPTEs and various mental health parameters. The survey utilized standard assessment tools including the PHQ-9, GAD-7, SPRINT, and AUDIT-C alongside measures of life satisfaction, emotional numbing and demographic questions. The prevalence of symptoms of Major Depressive Disorder (24 %), Generalized Anxiety Disorder (13 %), and probable PTSD (13 %) among 1264 fire and emergency personnel are higher than those observed in the general population. High exposure to PPTEs was associated with higher odds of reporting moderate-to-severe symptoms of Major Depressive Disorder, Generalized Anxiety Disorder, and probable PTSD. Also of note were findings revealing a high prevalence of potentially hazardous drinking patterns (68 %). Highlights a critical need for effective mental ill health prevention and support programs tailored to fire and emergency personnel, considering their high exposure to PPTEs and elevated risk of experiencing symptoms of Major Depressive Disorder, Generalized Anxiety Disorder, and PTSD.

The Human Interference Scoring System (HISS) is a novel food-based diet-quality-classification system based on the existing NOVA method. HISS involves food and fluid allocation into categories from digital imagery based on food processing levels, followed by meal plan analysis using food-servings quantification. The primary purpose of this work was to evaluate the reliability of HISS. Trained nutrition professionals analyzed digital photographs from five hypothetical 24 h food recalls and categorized foods into one of four HISS categories. A secondary purpose was to assess the nutrient composition of the food recalls and other selected foods from the HISS categories. Participants effectively categorized foods into HISS categories, with only minor discrepancies noted. High inter-rater reliability was observed in the outer HISS categories: unprocessed and ultra-processed foods. Ultra-processed items consistently displayed elevated energy, carbohydrates, and sugar compared to unprocessed foods, while unprocessed foods exhibited notably higher dietary fiber. This study introduces the HISS as a potentially useful tool for quantifying a food-quality-based system using digital-photography-based assessments. Its high inter-rater reliability and ability to capture relationships between food processing levels and nutrient composition make it a promising method for assessing dietary habits and food quality.

Ultra-processed foods (UPF), defined using frameworks such as NOVA, are increasingly linked to adverse health outcomes, driving interest in ways to identify and monitor their consumption. Artificial intelligence (AI) offers potential, yet its application in classifying UPF remains underexamined. To address this gap, we conducted a scoping review mapping how AI has been used, focusing on techniques, input data, classification frameworks, accuracy and application. Studies were eligible if peer-reviewed, published in English (2015–2025), and they applied AI approaches to assess or classify UPF using recognised or study-specific frameworks. A systematic search in May 2025 across PubMed, Scopus, Medline and CINAHL identified 954 unique records with eight ultimately meeting the inclusion criteria; one additional study was added in October following an updated search after peer review. Records were independently screened and extracted by two reviewers. Extracted data covered AI methods, input types, frameworks, outputs, validation and context. Studies used diverse techniques, including random forest classifiers, large language models and rule-based systems, applied across various contexts. Four studies explored practical settings: two assessed consumption or purchasing behaviours, and two developed substitution tools for healthier options. All relied on NOVA or modified versions to categorise processing. Several studies reported predictive accuracy, with F1 scores from 0·86 to 0·98, while another showed alignment between clusters and NOVA categories. Findings highlight the potential of AI tools to improve dietary monitoring and the need for further development of real-time methods and validation to support public health.

Background/Objectives: The global epidemic of type 2 diabetes (T2D) is a critical public health issue, particularly in New Zealand, where prevalence rates are high, especially among Māori and Pacific people. Recent research indicates that dietary interventions, particularly carbohydrate reduction, can lead to the remission or reversal of T2D. However, little is known about how such approaches perform when implemented in routine New Zealand primary care, particularly within high-risk and underserved populations. This study aimed to evaluate changes in HbA1c, diabetes status, and cardiometabolic outcomes among adults with prediabetes and T2D engaged in such a model of care. Methods: This study reports findings from a retrospective, observational, real-world, multi-site clinical audit (service evaluation) of a holistic model of care implemented in three primary care practices in New Zealand. The model of care is characterised by a three-pronged approach: whole food, carbohydrate reduction; a health-coach, behaviour-change-based delivery approach; and community- or peer-based initiatives. Audit data from 106 patients with prediabetes (PD) and T2D were analysed (median follow-up 19 months; IQR 6–32) to assess changes in glycosylated haemoglobin (HbA1c) levels, diabetes status, and cardiometabolic outcomes. Results: We observed an overall reduction in HbA1c (median change −3 mmol/mol (IQR: −7 to 3), p = 0.004), with 32% of patients with T2D at baseline achieving reversal and 44% of those with PD attaining normoglycaemia at final follow-up. Weight loss was associated with greater HbA1c reduction (0.56 mmol/mol decrease per kg lost) and additional improvements seen in lowered alanine aminotransferase (ALT). HDL cholesterol showed a small decline (r = 0.31), and triglycerides and blood pressure showed no significant change, indicating that these measures remained broadly stable over the evaluation period. Conclusions: Given the retrospective and uncontrolled audit design, findings should be interpreted with appropriate caution. However, the consistent improvements observed across multiple practices suggest that carbohydrate-reduction strategies within holistic models of care can meaningfully improve diabetes outcomes in real-world primary care settings. Future research should evaluate longer-term sustainability, implementation fidelity, and the applicability of this model at scale, particularly for Māori and Pacific communities.

This study explores a novel healthcare model employed in the primary care setting integrating a carbohydrate‐reduction dietary approach and health coaching for managing prediabetes (PD) and Type 2 diabetes (T2D) in New Zealand. Using qualitative methods, we conducted focus groups with 46 patients and individual interviews with health coaches and general practitioners across two regions. Five major themes emerged from inductive thematic analysis: reduced carbohydrate lifestyles, health coaching, implementation, empowerment, and sustainability. Patients reported significant health improvements, including weight loss, reduced medication burden, and increased energy. Challenges included resistance from some medical professionals and negative public perceptions. Health coaching played a crucial role in patient care, providing individualised support and enhancing health literacy. The study found that this model both improved patient outcomes and also alleviated the burden on healthcare professionals by managing time‐intensive aspects of patient care. Barriers to the adoption of this model include scepticism about low‐carbohydrate diets and the need for more education and awareness among healthcare professionals. The findings suggest that this healthcare model has the potential to transform the management of PD and T2D in primary care, shifting patients from lifelong medication dependence to significant health improvements and potential disease remission or reversal.