TY - JOUR

T1 - Author Correction

T2 - The role of self-care and self-compassion in networks of resilience and stress among healthcare professionals (Scientific Reports, (2025), 15, 1, (18545), 10.1038/s41598-025-01111-1)

AU - Pank, Carolina

AU - von Boros, Lisa

AU - Lieb, Klaus

AU - Dalkner, Nina

AU - Egger-Lampl, Sebastian

AU - Lehr, Dirk

AU - Schäfer, Sarah K.

AU - Tüscher, Oliver

AU - Wessa, Michèle

N1 - Publisher Copyright: © The Author(s) 2025.

PY - 2025/12/30

Y1 - 2025/12/30

N2 - Correction to: Scientific Reportshttps://doi.org/10.1038/s41598-025-01111-1, published online 27 May 2025 The original version of this article contained errors in the reported numerical values from the third network model. As a result, in the Abstract, where: “Cross-sectional data from HCPs in Germany were collected from June-July 2023. Analyses of 212 HCPs (age 41.63 [21–68] years; 81.60% women) revealed self-compassion as the most important factor across all networks, while the importance of self-care showed through individual connections to crucial factors like mental health problems and work-life balance.” now reads: “Cross-sectional data from HCPs in Germany were collected in April 2023. Analyses of 212 HCPs (age 41.63 [21–68] years; 81.60% women) revealed self-compassion as the most important factor across all networks, while the importance of self-care showed through individual connections to crucial factors like mental health problems and work-life balance.” In addition, under the Results section, subheading ‘Network on resilience-related factors and work-related outcomes’, where: “Of 78 possible edges, 48 were included, showing a small overall mean weight of 0.03. Of the included edges, 16 showed negative associations. The strongest associations were found between (1) emotional exhaustion and depersonalization (r = 0.35), (2) self-care and mental health problems (r = − 0.34), and (3) emotional exhaustion and work-life balance (r = − 0.34). Bootstrapped confidence intervals of edge weight parameters showed minor variability (see supplementary material Figure S5). The CS-coefficient indicated stable edges with an edge weight accuracy of CS cor=0.7 = 0.59. Setting the hyperparameter to γ = 0.5 did not alter the overall network characteristics.” now reads: “Of 55 possible edges, 35 were included, showing a small overall mean weight of 0.05. Of the included edges, 7 showed negative associations. The strongest associations were found between (1) emotional exhaustion and work-life balance (r = − 0.40), (2) emotional exhaustion and depersonalization (r = 0.37), and (3) work engagement and personal accomplishment (r = 0.32). Bootstrapped confidence intervals of edge weight parameters showed minor variability (see supplementary material Figure S5). The CS-coefficient indicated stable edges with an edge weight accuracy of CS cor=0.7 = 0.59. Setting the hyperparameter to γ = 0.5 did not alter the overall network characteristics.” And where: “Centrality indices showed that, again, self-compassion had the highest centrality strength (1.11), followed by work engagement (0.99) and emotional exhaustion (0.92). Accuracy analyses revealed a CS-coefficient of CS cor=0.7 = 0.52 for strength centrality, indicating that the centrality indices are stable, and the differences can be reliably interpreted. Details of edge weights and centrality strength can be found in the supplementary material Table S3.” now reads: “Centrality indices showed that, again, self-compassion had the highest centrality strength (1.11), followed by work engagement (0.99) and emotional exhaustion (0.92). Accuracy analyses revealed a CS-coefficient of CS cor=0.7 = 0.44 for strength centrality, indicating moderate stability of the centrality estimates. Details of edge weights and centrality strength can be found in the supplementary material Table S3.” The original version of this Article has been corrected.

AB - Correction to: Scientific Reportshttps://doi.org/10.1038/s41598-025-01111-1, published online 27 May 2025 The original version of this article contained errors in the reported numerical values from the third network model. As a result, in the Abstract, where: “Cross-sectional data from HCPs in Germany were collected from June-July 2023. Analyses of 212 HCPs (age 41.63 [21–68] years; 81.60% women) revealed self-compassion as the most important factor across all networks, while the importance of self-care showed through individual connections to crucial factors like mental health problems and work-life balance.” now reads: “Cross-sectional data from HCPs in Germany were collected in April 2023. Analyses of 212 HCPs (age 41.63 [21–68] years; 81.60% women) revealed self-compassion as the most important factor across all networks, while the importance of self-care showed through individual connections to crucial factors like mental health problems and work-life balance.” In addition, under the Results section, subheading ‘Network on resilience-related factors and work-related outcomes’, where: “Of 78 possible edges, 48 were included, showing a small overall mean weight of 0.03. Of the included edges, 16 showed negative associations. The strongest associations were found between (1) emotional exhaustion and depersonalization (r = 0.35), (2) self-care and mental health problems (r = − 0.34), and (3) emotional exhaustion and work-life balance (r = − 0.34). Bootstrapped confidence intervals of edge weight parameters showed minor variability (see supplementary material Figure S5). The CS-coefficient indicated stable edges with an edge weight accuracy of CS cor=0.7 = 0.59. Setting the hyperparameter to γ = 0.5 did not alter the overall network characteristics.” now reads: “Of 55 possible edges, 35 were included, showing a small overall mean weight of 0.05. Of the included edges, 7 showed negative associations. The strongest associations were found between (1) emotional exhaustion and work-life balance (r = − 0.40), (2) emotional exhaustion and depersonalization (r = 0.37), and (3) work engagement and personal accomplishment (r = 0.32). Bootstrapped confidence intervals of edge weight parameters showed minor variability (see supplementary material Figure S5). The CS-coefficient indicated stable edges with an edge weight accuracy of CS cor=0.7 = 0.59. Setting the hyperparameter to γ = 0.5 did not alter the overall network characteristics.” And where: “Centrality indices showed that, again, self-compassion had the highest centrality strength (1.11), followed by work engagement (0.99) and emotional exhaustion (0.92). Accuracy analyses revealed a CS-coefficient of CS cor=0.7 = 0.52 for strength centrality, indicating that the centrality indices are stable, and the differences can be reliably interpreted. Details of edge weights and centrality strength can be found in the supplementary material Table S3.” now reads: “Centrality indices showed that, again, self-compassion had the highest centrality strength (1.11), followed by work engagement (0.99) and emotional exhaustion (0.92). Accuracy analyses revealed a CS-coefficient of CS cor=0.7 = 0.44 for strength centrality, indicating moderate stability of the centrality estimates. Details of edge weights and centrality strength can be found in the supplementary material Table S3.” The original version of this Article has been corrected.

UR - https://www.scopus.com/pages/publications/105026435653

U2 - 10.1038/s41598-025-33287-x

DO - 10.1038/s41598-025-33287-x

M3 - Comments / Debate / Reports

C2 - 41469436

AN - SCOPUS:105026435653

VL - 15

SP - 45737

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 1

M1 - 45737

ER -