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Resilience among nurses working in maternity wards in Bangladesh: A qualitative study Sharma, Aarav; Patel, Priya; Mehta, Rohan
Lentera Perawat Vol. 6 No. 4 (2025): October - Desember
Publisher : STIKes Al-Ma'arif Baturaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52235/lp.v6i4.590

Abstract

Background: Nurses working in maternity wards in Bangladesh face complex clinical and emotional challenges due to maternal complications, high patient loads, and limited resources. Resilience has been recognized as a critical factor in helping nurses cope with stress, adapt to demanding environments, and sustain compassionate care. Objective: This study aimed to explore and describe the experiences of resilience among nurses working in maternity wards in Bangladesh. Methods: A qualitative interpretive descriptive design was employed, guided by Thorne’s framework. Purposive sampling recruited eight registered nurses from maternity units, including labor and delivery, postnatal, and maternal high-dependency wards, in tertiary hospitals. Semi-structured individual interviews were conducted between October 2020 and January 2021, lasting 30–90 minutes, and were audio recorded. Data were analyzed inductively to identify themes reflecting resilience experiences. Trustworthiness was ensured using COREQ guidelines. Results: Three overarching themes emerged: (1) The transition period, reflecting anxiety and lack of preparedness during the early stages of maternity nursing; (2) Gaining trust of mothers, families, and colleagues, highlighting the challenges of acceptance, communication, and professional recognition; and (3) Having a positive mindset, emphasizing psychological resilience, optimism, and self-care practices that enabled nurses to cope with workplace stress. Conclusion: Resilience among maternity nurses in Bangladesh is developed through a dynamic interplay of personal adaptation, social support, and psychological coping strategies. Strengthening structured mentorship, fostering supportive workplace cultures, and integrating resilience training into professional development are crucial to enhance maternal care quality and reduce burnout among maternity nurses.
ADAPTIVE COMPLEXITY IN LIVING SYSTEMS: INTEGRATING ECOLOGICAL DYNAMICS WITH NONLINEAR MATHEMATICAL MODELING Sharma, Aarav; Lim, Sofia; Schmidt, Daniel
Research of Scientia Naturalis Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i1.3541

Abstract

Adaptive complexity is a defining feature of living systems, where nonlinear interactions, feedback mechanisms, and environmental variability shape dynamic behaviors that cannot be adequately explained through linear models. Ecological research increasingly recognizes the limitations of equilibrium-based approaches, yet a coherent integration of ecological dynamics with nonlinear mathematical modeling remains underdeveloped. This study aims to develop an integrative framework that captures adaptive complexity by combining empirical ecological data with nonlinear dynamical systems analysis. The research employs a mixed-methods design, incorporating secondary ecological datasets, computational modeling, and techniques such as bifurcation and sensitivity analysis to examine system behavior under varying conditions. Results demonstrate that ecological systems exhibit multi-stability, threshold effects, and chaotic dynamics, with environmental variability and interaction intensity significantly influencing system transitions. Nonlinear models successfully capture emergent behaviors and reveal critical tipping points that are not identifiable through linear approaches. These findings highlight that adaptive complexity operates as an organizing principle rather than a peripheral characteristic of living systems. The study concludes that integrating ecological dynamics with nonlinear mathematical modeling enhances both theoretical understanding and practical predictive capacity, offering a robust framework for analyzing resilience and transformation in ecological systems.