Using simulation to prepare social work students for field education

Jefferies, Gerard; Davis, Cindy; Mason, Jonathan; Yadav, Raj

doi:10.1080/02615479.2023.2185219

Cited by 7 publications

(1 citation statement)

References 32 publications

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“…Therefore, a logic that puts organizational interests and bureaucratic needs first undermines the strong principles of professionalism that are based on experience-based wisdom and moral values (Blome & Espvall, 2023). In terms of technological challenges, in recent years, the increasing reliance on technology in everyday social work, including the use of algorithmic decision-making systems (James, Lal, Liao, Magee, & Soldatic, 2023) has transformed the way social pedagogy must be practiced now, e.g., the use of simulation activities (Jefferies, Davis, Mason, & Yadav, 2023) so that potential social workers can engage effectively with the realities of sociotechnical practices (Taylor-Beswick, 2023). Consequently, these multifaceted challenges can be stressful, demanding students of social work to exhibit adaptive capabilities, effectively engage with technologies and build on their mastery by contemplating their past experiences in order to contend with the complexities of their training and future professional roles.…”

Section: Introductionmentioning

confidence: 99%

Associations between resilience, technostress and reflective capacity in social pedagogy students: A structural equation modeling methodology

Baisarinova,

Toleubekova

2023

JEELR

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Understanding factors affecting student academic and life outcomes may be critical for social work programs and educational institutions. Therefore, this study aims to explore the relationships between resilience, technostress and reflective capacity among social work students. A cross-sectional non-experimental research design was used. One hundred and sixty-three social work students completed an online survey measuring technostress level, reflective capacity and psychological resilience. Data were analyzed using Pearson’s to estimate linear correlations, confirmatory factor analysis to ensure model fit and structural equation modeling with covariates to reveal the relationships between the measured constructs. The results indicated that resilience had a direct and negative effect on technostress, a positive and direct effect on reflective capacity and a negative indirect effect on technostress through reflective capacity. This means that social work students with higher levels of resilience had lower levels of technostress related to technology use were more likely to engage in reflective practices and had lower levels of technostress due to improved reflective capacity. These findings emphasize the significance of resilience and reflective practices in mitigating the negative effects of technology use among social work students. However, researchers should be cautious about generalizing findings due to the sample’s limitations and future investigations should consider longitudinal data from diverse populations.

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