Kristianstad University

Although women’s autonomy is key to patient care, the extent to which patients exercise autonomy during clinical consultations in resource-limited contexts such as Zambia remains unknown. This study aimed to examine women’s experiences of autonomy in decision-making and respectful treatment in Zambian maternal healthcare. The study was conducted in Lusaka and used a cross-sectional survey design. The sample consisted of 305 women who were conveniently recruited. Data was collected using a questionnaire consisting of sociodemographic data and two validated instruments: the 7-item Mothers Autonomy and Decision-Making Scale (MADM), and the 14-item Measure of Respect (MOR) index. Higher levels of decision-making autonomy, according to MADM, are significantly associated with higher levels of perceived respect in maternal healthcare settings, according to MOR (p < .001). Furthermore, higher education was found to correlate significantly with higher autonomy and respect (p < .001). Both instruments showed an excellent (MADM) and good (MOR) internal consistency among this sample (Cronbach’s alpha = 0.959 and 0.851, respectively). Overall, while there’s significant variation in responses, there is a slight tendency towards positive experiences in both autonomy and respect in maternal healthcare settings. Health authorities should promote the implementation of autonomous and respectful care for all women, regardless of socioeconomic or educational background, and provide a supportive environment that fosters user participation in decision-making. Patient autonomy is a core ethical principle in enhancing patient-centered care and influencing healthcare decision-making. However, the extent of maternal autonomy practice in resource-limited contexts like Zambia remains understudied. This study reveals significant disparities in Zambian women’s experiences of autonomy and respectful care, influenced by sociodemographic factors and provider type – indicating potential systemic biases and structural inequities in Zambian maternity care. Higher education, employment, marital status, and having children correlate with increased autonomy and respect. Furthermore, women reported higher levels of autonomy and respect when consulting OB/GYNs than when consulting midwives, contrary to findings in other contexts.

The aim of this study is to enhance understanding of how social robots affect the emotional labour of frontline employees in the service sector. It employs a qualitative case study approach, using semi-structured interviews, observations, and document analysis. The study participants include frontline employees, managers, and HR officers at an amusement park in Sweden. Our findings demonstrate that the introduction of robots changed the conditions for emotional labour by facilitating work in some cases, complicating it in others, and in some instances fundamentally transforming it. A key finding is the increased theatricalisation of labour, whereby frontline employees bring robots to life through performance and role-playing. This behaviour was shaped by overarching organizational expectations rather than direct managerial instructions. The robots also shifted the balance between onstage and backstage roles, as employees spent more time performing in customer-facing roles and less time recovering backstage. This study contributes to the development of emotional labour theory by cross-fertilizing it with Goffman's dramaturgical perspective, which adds dimensions related to self-presentation and the adaptation of emotions and behaviour. By focusing on employees with experience working alongside robots, the study offers new insights into how robots reshape the emotional and social interactions of frontline service work.

The rapid spread of climate change misinformation across digital platforms undermines scientific literacy, public trust, and evidence-based policy action. Advances in Natural Language Processing (NLP) and Large Language Models (LLMs) create new opportunities for automating the detection and correction of misleading climate-related narratives. This study presents a multi-stage system that employs state-of-the-art large language models such as Generative Pre-trained Transformer 4 (GPT-4), Large Language Model Meta AI (LLaMA) version 3 (LLaMA-3), and RoBERTa-large (Robustly optimized BERT pretraining approach large) to identify, classify, and generate scientifically grounded corrections for climate misinformation. The system integrates several complementary techniques, including transformer-based text classification, semantic similarity scoring using Sentence-BERT, stance detection, and retrieval-augmented generation (RAG) for evidence-grounded debunking. Misinformation instances are detected through a fine-tuned RoBERTa-Multi-Genre Natural Language Inference (MNLI) classifier (RoBERTa-MNLI), grouped using BERTopic, and verified against curated climate-science knowledge sources using BM25 and dense retrieval via FAISS (Facebook AI Similarity Search). The debunking component employs RAG-enhanced GPT-4 to produce accurate and persuasive counter-messages aligned with authoritative scientific reports such as those from the Intergovernmental Panel on Climate Change (IPCC). A diverse dataset of climate misinformation categories covering denialism, cherry-picking of data, false causation narratives, and misleading comparisons is compiled for evaluation. Benchmarking experiments demonstrate that LLM-based models substantially outperform traditional machine-learning baselines such as Support Vector Machines, Logistic Regression, and Random Forests in precision, contextual understanding, and robustness to linguistic variation. Expert assessment further shows that generated debunking messages exhibit higher clarity, scientific accuracy, and persuasive effectiveness compared to conventional fact-checking text. These results highlight the potential of advanced LLM-driven pipelines to provide scalable, real-time mitigation of climate misinformation while offering guidelines for responsible deployment of AI-assisted debunking systems.

Aim: Freshwater rock pools are ephemeral and fragile habitats that support specialised animal taxa. While distributed worldwide, these habitats are usually neglected and overlooked. We used DNA metabarcoding and metaphylogeographic approaches to study inter and intraspecific tardigrade biodiversity to identify their biogeographic patterns to inform conservation measures. Location: Europe. Period: Present. Major Taxa Studied: Tardigrades. Methods: We conducted DNA metabarcoding targeting a fragment of the cytochrome oxidase subunit 1 gene of 163 freshwater rock pools from four sites in Sweden, Poland and Italy to analyse the effect of both short and long distance on their alpha diversity and genetic differentiation. Results: A total of 85 tardigrades OTUs (species-level clusters) were detected, with the most prevalent taxa across all samples being species from the genus Ramazzottius. OTUs richness was mostly driven by a negative relationship with rock pool depth and site-specific differences. For seven OTUs a phylogeographic analyses revealed a strong founder effect and a variable effect of geographic distance and climate on their population structures. Main Conclusions: The high biodiversity and presence of potential specialised taxa highlight the conservation value of freshwater rock pools. While some tardigrade species may be able to easily disperse and have a large geographic range, other species show isolation by distance patterns and their genetic diversity may be negatively affected by habitat loss. Our results call for a bigger effort in identifying, mapping, characterising and protecting freshwater rock pools as underlooked but important habitats.

Adaptive management (AM) is one approach to manage migratory waterbirds, but obstacles to the implementation of AM require adaptive capacities in the management system (rules, institutions, action situations). This study aims to examine the adaptive capacity of participatory goose management in Sweden. Considering the biophysical and institutional context, we analyzed how tangible, individual, and governance assets were associated with technical and social learning. Interviews with informants in the national council for geese, swans, and cranes, and local management groups (LMGs) were conducted, and documentation reviewed. Results revealed evidence of a local preparedness in areas with an LMG. Nevertheless, the study highlighted a need to formalize the evolving system, to consider a more systematic implementation of AM (including regulations allowing for adaptive responses), and to ensure stakeholder acceptance for management tools and visions. The study illustrates the need for a broad set of assets to ensure learning in participatory management.