Mid Sweden University
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Reciprocal effects between illicit drug use and mental health conditions among healthcare workers in Sweden : A one-year follow-up study
No full textBackground: Research suggests a comorbidity between illicit drug use and mental health conditions. However, it remains unclear whether illicit drug use serves as a risk factor for, or a consequence of, mental health conditions in healthcare workers (HCWs). This study aimed to 1) examine the prevalence of illicit drug use among HCWs in Sweden and 2) investigate the bidirectional relationship between illicit drug use and mental health conditions(i.e., depression and burnout). Methods: Data from the 2022 and 2023 Longitudinal Occupational Health Survey in Healthcare Sweden (LOHHCS) were used. The data included 3280 HCWs (50.3 % physicians and 49.7 % nurses). Questionnaires assessed illicit drug use frequency, burnout complaints (BAT-12), and depression (SCL-CD6). Cross-lagged panel models (CLPMs) were used to examine the reciprocal relationships over the two studied time-points between illicit drug use and mental health conditions. Results: The prevalence of illicit drug use in 2022 was 1.3 %, which increased slightly to 1.6 % one and a half years later, in 2023. Using two-wave panel data, results revealed a bidirectional effect between illicit drug use and burnout. However, while depression was associated with subsequent illicit drug use, the reversed association was not observed. Conclusions: These findings suggest that illicit drug use plays different roles in relation to burnout and depression among healthcare workers. This highlights the importance of integrated treatment strategies and preventive measures that address both illicit drug use and mental health conditions—especially burnout—simultaneously.
“The real life is to be with other people”: A social lens on immigrant integration through outdoor recreation
No full textThis article explores the role of domestic tourism, specifically outdoor recreational sports and events, in fostering immigrant integration, with a focus on Jämtland County in northern Sweden. While friluftsliv (outdoor recreation) holds a central place in Swedish leisure and domestic tourism culture, immigrants are often underrepresented in such activities. Rather than framing this solely as a matter of cultural difference, we investigate how immigrants perceive outdoor engagement, the barriers they encounter, and how social connections shaped through social capital can offer empowering, integrative experiences. The study draws on in-depth interviews with thirty-four immigrants, adding new knowledge to how outdoor recreation intersects with social capital, social sustainability, and integration. Our analysis highlights three key themes: belonging to outdoors, barriers to participation, and the role of sports and events in shaping social connections. We argue that outdoor engagement can support integration and enhance social sustainability, offering insights into both theory and inclusive practice in domestic tourism and recreation planning
DUALF-D : Disentangled dual-hyperprior approach for light field image compression
No full textLight field (LF) imaging captures spatial and angular information, offering a 4D scene representation enabling enhanced visual understanding. However, high dimensionality and redundancy across spatial and angular domains present major challenges for compression, particularly where storage, transmission bandwidth, or processing latency are constrained. We present a novel Variational Autoencoder (VAE)-based framework that explicitly disentangles spatial and angular features using two parallel latent branches. Each branch is coupled with an independent hyperprior model, allowing more precise distribution estimation for entropy coding and finer rate-distortion control. This dual-hyperprior structure enables the network to adaptively compress spatial and angular information based on their unique statistical characteristics, improving coding efficiency. To further enhance latent feature specialization and promote disentanglement, we introduce a mutual information-based regularization term that minimizes redundancy between the two branches while preserving feature diversity. Unlike prior methods relying on covariance-based penalties prone to collapse, our information-theoretic regularizer provides more stable and interpretable latent separation. Experimental results on publicly available LF datasets demonstrate our method achieves strong compression performance, yielding an average BD-PSNR gain of 2.91 dB over HEVC and high compression ratios (e.g., 200:1). Additionally, our design enables fast inference, with a total end-to-end time over 19x faster than the JPEG Pleno standard, making it well-suited for real-time and bandwidth-sensitive applications. By jointly leveraging disentangled representation learning, dual-hyperprior modeling, and information-theoretic regularization, our approach offers a scalable, effective solution for practical light field image compression
Environmental assessment in Estonia – A quest for an effective EA system
No full textThe effectiveness of Environmental Assessment (EA) has been intensively discussed in academia and practice. However, few studies provided a longitudinal analysis of a country or examined in-depth the interactions with the institutional context. This article aims to contribute to the understanding of EA-effectiveness by examining the interaction of EA with planning and decision-making institutions. Therefore, we present an in-depth historical analysis of how EA together with the planning and decision-making system evolved over time, using Estonia as a case. We developed an analytical framework to analyse the various effectiveness dimensions of EA (procedural, substantive, transactive, legitimacy, and knowledge& learning) and their interactions with the broader institutional setting of planning and decision-making, and conducted document analysis (of regulations, policies and evaluations), interviews and a focus group, reviewing the period between 1988 and 2024. Important findings include that the dominance of the effectiveness dimensions is dynamic characterized by the interaction between the EA-system and the broader institutional setting of the planning system. Furthermore, there is interaction between the different dimensions of effectiveness over time. This means that EA effectiveness cannot be fully understood by examining one single effectiveness dimension nor by considering EA in isolation; the broader institutional context must be considered. To improve EA effectiveness, it is crucial to acknowledge this, and to address multiple effectiveness dimensions as well as the broader institutional setting. Perhaps the key to enhancing EA effectiveness lies beyond EA itself, which aligns with its original role as an instrument to aid decision-making and planning.
Associations between healthcare workers' substance use and quality of care : Findings from a one-year Swedish follow-up study
No full textBackground: Problem drinking and illicit drug use among healthcare workers (i.e., physicians and nurses) may impair their attention and cognitive functioning, thereby increasing the risk of medical errors and diminishing the quality of patient care. Objective: To investigate the association between healthcare workers' problem drinking and illicit drug use with subsequent self-rated quality of care provided. Design: A two-wave longitudinal observational study. Methods: Panel data were drawn from the Longitudinal Occupational Health Survey in Healthcare Sweden (LOHHCS), collected in 2022 (baseline) and 2023 (follow-up), encompassing a sample of 3280 healthcare workers. Questionnaires included problem drinking, illicit drug use (cannabis and stimulants), and self-rated quality of care they provide to patients. Logistic regression models analysed relationships between the study variables. Results: At baseline, the prevalence of problem drinking was 3.8 %, and illicit drug use was 1.3 %. Both problem drinking (OR = 1.93, 95 % CI = 1.28–3.02) and illicit drug use (OR = 2.07, 95 % CI = 1.00–4.29) were significantly associated with lower self-rated quality of care provided at follow-up, after adjustment for confounding variables. Conclusions: This novel longitudinal study shows that healthcare workers reporting substance use at baseline were about twice as likely to report providing poor quality of care one year later. These findings are of clinical relevance and highlight the need for targeted preventive measures and interventions to safeguard the health and well-being of healthcare workers while maintaining quality standards in patient care.
Adapting biodiversity conservation in agriculture and forestry to projected climate change in temperate and boreal regions : A synthesis
No full textClimate change creates new challenges for biodiversity conservation. Numerous conservation approaches have been advocated in response and there is a need to compile these into a readily accessible format. We systematically searched scientific literature to summarize recommendations that previous review papers have given for conserving biodiversity in the face of both direct and indirect effects of climate change. As indirect effects of climate change, we considered altered land management and habitat loss, increased disturbances and extreme events, and pests and invasive species. We included recommendations targeting production landscapes dominated by agriculture or forestry in temperate and boreal regions. We found 285 relevant reviews, which in turn cited 874 original research papers as support for the recommendations given. Of the summarized recommendations, 35 % considered direct and 58 % indirect effects of climate change, while 7 % considered both. Indirect effects were considered more frequently in recommendations applicable to agriculture than forestry dominated landscapes. Frequent recommendations were increasing landscape habitat diversity or connectivity, mitigating habitat deterioration, restoring degraded habitats, and adapting management methods in both forestry and agriculture. Most of the recommendations were similar to or consistent with traditional conservation practices, while novel, climate-change specific recommendations were less frequent. We conclude that there is a wealth of research on how to maintain biodiversity in agriculture and forestry dominated landscapes in a world with a warming climate. The summarized recommendations provide a starting point for planning conservation, and the attached database with all considered reviews and original research papers can be used as a source for evidence-based management.
Few-shot learning and explainable AI for colon cancer histopathology : A prototypical network with multi-technique interpretability
No full textBackground: Colon cancer diagnosis from histopathology is challenging due to limited annotated data and the lack of interpretability in deep models. Objective: We present a data-efficient framework combining few-shot learning and explainable AI for accurate and transparent diagnosis. Methods: A Prototypical Network with a ConvNeXt-Tiny backbone was trained on small colon-tissue image sets. Explanations from Grad-CAM and LIME were validated by a pathologist, and generalization was tested on an external dataset. Results: The model achieved 98.5 % accuracy in-domain and 90 % on the EBHI dataset, showing strong generalization. Conclusions: This few-shot and explainable model performs well with minimal data and generates clinically interpretable visual outputs, supporting its potential for reliable colon cancer diagnosis.
Water hyacinth (Eichhornia crassipes) biomass characterization for a potential exploration as an agriculture soil enhancer : Linking multi-location biogeochemical profiles to ecotoxicological safety
No full textWater hyacinth (WH) is an invasive aquatic species for which no universal biomass management strategy exists, although many developing countries use it in agriculture with limited understanding of its potential environmental impacts. As WH is an effective bioaccumulator it is essential to assess its composition and quantify potentially harmful elements before this surplus green biomass can be effectively valorised. Determining the thresholds for their effects is crucial to define safe and sustainable uses. In this context, this study characterized WH biomass from six Portuguese locations (four northern and two southern), focusing on nutrient and potentially toxic element (PTE) profiles, sugar, protein, and structural composition. Furthermore, the ecotoxicological profile of aqueous extracts from each WH biomass was evaluated using several freshwater species ( Raphidocelis subcapitata, Brachionus calyciflorus, Daphnia magna, and Danio rerio ) and multiple endpoints, to benchmark safe agricultural application rates. Structural analysis revealed tissue type (leaves, floaters, roots) had greater influence than sampling location, with roots showing highest absorbance linked to lignin, proteins, and cellulose. These wall components provide metal-binding sites, explaining root PTE levels being higher than other tissues. Elemental composition showed high primary nutrients (e.g., potassium, phosphorus), meeting EU requirements for organic soil improvers (EU Regulation 2019/1009). Whole-plant WH water extracts had high conductivity (≥ 6.98 mS/cm), nutrient and PTE concentrations, and caused adverse effects on all aquatic species. No clear toxicity ranking emerged, though Bico and Pateira extracts were least toxic, and Sorraia extract most severe (algal inhibition, zooplankton mortality, zebrafish effects at 0.78 % dilution). The findings indicate that WH biomass incorporation into soils should be considered on a site-specific basis, owing to variations in PTEs accumulation across locations, requiring contaminant screening and regulatory guidance before large-scale use. The results evidenced multispecies, multi-endpoint ecotoxicity that might justify the need for dilution strategies and controlled application rates of WH biomass on soils to minimize putative downstream impacts.
Enhancing carob flour (Ceratonia siliqua L.) for by-product utilization in food industries : Carob syrup production, functional profiling and application
No full textThe focus on by-product valorization in the food industry, particularly from the carob pod, underscores a commitment to sustainability and resource efficiency. This fruit, sourced from the leguminous evergreen carob tree (Ceratonia siliqua L.), is renowned for its adaptable flavour and nutritional value, in Mediterranean regions such as Portugal. Its production yields significant by-products, presenting environmental challenges when not managed efficiently. Innovative approaches, including integral carob flour production, aim to optimize utilization while minimizing waste and energy consumption. This study repurposed carob waste to produce novel, value-added ingredients like carob syrup, by thermal hydrolysis of integral carob flour using water at 1:3 solid-to-liquid ratio - obtaining up to 50 % solubility yield. The resulting syrup exhibited 72 % °Brix, a melting temperature (Tm) of approximately 130 °C and predominantly viscous behavior with minimal elastic (solid-like) response. Lastly, the syrup was incorporated into a carob-based brigadeiro, replacing conventional glucose-fructose syrup. Simulated gastrointestinal digestion revealed enhanced bioaccessibility of sugars and phenolics, and increased antioxidant activity during the intestinal phase. Despite sugar availability, the prebiotic activity of the syrup decreased when embedded in the brigadeiro matrix, potentially due to interactions with polyphenols or organic acids. Cytotoxicity and permeability assays confirmed safety at ≤0.5 % (w/v) and supported intestinal barrier integrity. These findings support the use of integral carob flour for producing multifunctional ingredients, contributing to circular economy models while meeting consumer demands for healthier, sustainable food products.
AI for colon cancer : A focus on classification, detection, and predictive modeling
No full textPurpose: Artificial Intelligence (AI) is increasingly recognized for its potential in improving the detection, classification, prediction, and segmentation of colon cancer. Yet, the reliability of these applications depends on the quality and completeness of the underlying studies. This systematic review evaluates the current state of AI applications in colon cancer research, focusing on their impact on diagnostic accuracy, treatment planning, and patient outcomes. Methods: A comprehensive search was conducted in PubMed, Scopus, and Web of Science for articles published between 2020 and 2024. The quality of the included studies was assessed using standardized criteria. A meta-analysis was performed where applicable, and a subgroup analysis was conducted based on the type of AI technology (e.g., deep learning, machine learning) and its application (detection, classification, etc.). Additionally, we recorded whether each study incorporated Explainable AI (XAI) techniques or Generative AI (e.g., GANs) as part of its methodology. Results: In 80 articles, AI models showed significant improvements in diagnostic accuracy, particularly in polyp detection during colonoscopies and histopathological analysis. Deep learning approaches often outperformed traditional methods. However, clinical integration remains challenging due to data and validation gaps. Conclusion: AI holds great promise in colon cancer diagnosis and treatment. Future work should focus on integrating AI tools into clinical workflows through explainable models and standardized validation.