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1.
Health and well-being of military nurses in high-reliability, high-stress environments : a qualitative study in the slovenian armed forces
Zlatko Kvržić, Mirko Prosen, 2026, original scientific article

Abstract: Aim: To investigate how female military nurses experience high-reliability, high-stress environments and how these conditionsshape their well-being.Background: Military nursing involves complex demands that extend beyond clinical care, including dual professional roles,operational unpredictability, and gendered expectations. These pressures can undermine physical, psychological, and social well-being, yet the lived experiences of military nurses, particularly women, remain underexplored.Design: A qualitative descriptive design was used.Methods: Ten female military nurses were recruited through purposive sampling and interviewed individually in semi-structuredonline interviews. Data were analysed using qualitative content analysis. Trustworthiness was ensured through reflexive coding,an audit trail, and adherence to COREQ guidelines.Results: Five overarching categories captured the factors shaping well-being: organisational and structural demands; high-stressoperational environments; emotional and psychological burden; coping and resilience; and gendered identity and work–familybalance. Participants described constrained autonomy, communication gaps, and role ambiguity within hierarchical structures.Psychological pressures were heightened by moral tensions, responsibility for colleagues, and expectations of emotional control.Coping relied mainly on informal peer support, as formal services were rarely used due to stigma. Gendered norms and familyresponsibilities further influenced well-being and career decisions.Conclusion: Military nurse well-being is shaped less by individual resilience and more by organisational culture, operationaldemands, and gendered expectations. Addressing these systemic factors is essential for sustaining the military nursing workforce.Implication for Nursing: Strengthening leadership support, communication, psychological safety, and professional autonomymay improve working conditions and support nurses’ well-being in demanding operational contexts.Implications for Health Policy: Policies should promote supportive organisational cultures, reduce stigma around help-seeking,and facilitate work–family reconciliation to sustain and retain the military nursing workforce.
Keywords: military medicine, occupational health, psychological stress, qualitative research, work–family conflict, work environment
Published in RUP: 17.04.2026; Views: 318; Downloads: 8
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2.
Evaluation of changes in prediction modelling in biomedicine using systematic reviews
Lara Lusa, Franziska Kappenberg, Gary S. Collins, Matthias Schmid, Willi Sauerbrei, Jörg Rahnenführer, 2025, original scientific article

Abstract: The number of prediction models proposed in the biomedical literature has been growing year on year. In the last few years there has been an increasing attention to the changes occurring in the prediction modeling landscape. It is suggested that machine learning techniques are becoming more popular to develop prediction models to exploit complex data structures, higher-dimensional predictor spaces, very large number of participants, heterogeneous subgroups, with the ability to capture higher-order interactions. We examine the changes in modelling practices by investigating a selection of systematic reviews on prediction models published in the biomedical literature. We selected systematic reviews published between 2020 and 2022 which included at least 50 prediction models. Information was extracted guided by the CHARMS checklist. Time trends were explored using the models published since 2005. We identified 8 reviews, which included 1448 prediction models published in 887 papers. The average number of study participants and outcome events increased considerably between 2015 and 2019 but remained stable afterwards. The number of candidate and final predictors did not noticeably increase over the study period, with a few recent studies using very large numbers of predictors. Internal validation and reporting of discrimination measures became more common, but assessing calibration and carrying out external validation were less common. Information about missing values was not reported in about half of the papers, however the use of imputation methods increased. There was no sign of an increase in using of machine learning methods. Overall, most of the findings were heterogeneous across reviews. Our findings indicate that changes in the prediction modeling landscape in biomedicine are smaller than expected and that poor reporting is still common; adherence to well established best practice recommendations from the traditional biostatistics literature is still needed. For machine learning best practice recommendations are still missing, whereas such recommendations are available in the traditional biostatistics literature, but adherence is still inadequate.
Keywords: predictive model, medicine, changes
Published in RUP: 14.10.2025; Views: 583; Downloads: 9
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