The significance of the indirect ef

The significance of the indirect effects and the contrasts were determined via bootstrapping, a computationally extensive procedure (bootstrap samples: 5000) generating an empirical based estimation of the sampling distribution, which in turn is used for calculating bias corrected confidence intervals (CI)15. If a CI is completely below or above zero, the respective effect is significant15. Based on theory2, we controlled for demographics (age), functional health (physical quality of life) and illness type (cancer site; included in all analyses among the total sample). Additionally, we controlled for the visibility of the disease21. Gender was highly confounded with cancer type (prostate: all male; breast: all female except 3 patients) and thus not included. Robustness of effects was verified by replicating the findings across samples and reporting 95 % confidence intervals33. Multicollinearity was checked for each model (variance inflation factors ≤ 2.2). Multivariate outliers, detected via Mahalanobis distance exceeding the critical value of the chi-square-distribution with p < .001, were rare (≤ 1.4 %) and kept in the samples. Alpha was two-sided and set at .05. Listwise deletion was applied for all analyses, which were performed by SPSS 24 and the macro PROCESS32.

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结果 (简体中文) 1: [复制]

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间接影响和对比的重要性是通过自举确定的， 大量计算过程（引导样本：5000）生成基于经验的采样分布估计值，该估计值又用于计算偏差校正的置信区间（CI）15。如果CI完全低于零或高于零，则各自的影响是显着的15。基于理论2，我们控制了人口统计学（年龄），功能健康（生活的身体质量）和疾病类型（癌症部位；包括在总样本中的所有分析中）。此外，我们控制了疾病的可见性21。性别与癌症类型高度混淆（前列腺癌：所有男性；乳腺癌：除3名患者以外的所有女性），因此不包括在内。通过在样本之间复制发现并验证结果的鲁棒性 报告置信区间为95％33。检查每个模型的多重共线性（方差膨胀因子≤2.2）。通过马哈拉诺比斯距离检测到的多变量离群值 极少（≤1.4％）， 并保留在样本中，该离群值超过卡方分布的临界值且p <.001 。Alpha是双面的，设置为0.05。按列表删除适用于所有分析，这些分析由SPSS 24和宏PROCESS32执行。

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结果 (简体中文) 2:[复制]

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The significance of the indirect effects and the contrasts were determined via bootstrapping, a computationally extensive procedure (bootstrap samples: 5000) generating an empirical based estimation of the sampling distribution, which in turn is used for calculating bias corrected confidence intervals (CI)15. If a CI is completely below or above zero, the respective effect is significant15. Based on theory2, we controlled for demographics (age), functional health (physical quality of life) and illness type (cancer site; included in all analyses among the total sample). Additionally, we controlled for the visibility of the disease21. Gender was highly confounded with cancer type (prostate: all male; breast: all female except 3 patients) and thus not included. Robustness of effects was verified by replicating the findings across samples and reporting 95 % confidence intervals33. Multicollinearity was checked for each model (variance inflation factors ≤ 2.2). Multivariate outliers, detected via Mahalanobis distance exceeding the critical value of the chi-square-distribution with p < .001, were rare (≤ 1.4 %) and kept in the samples. Alpha was two-sided and set at .05. Listwise deletion was applied for all analyses, which were performed by SPSS 24 and the macro PROCESS32.

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结果 (简体中文) 3:[复制]

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通过bootstrapping，a 计算广泛的过程（bootstrap samples:5000），生成基于经验的抽样分布估计，进而用于计算偏差校正置信区间（CI）15。如果CI完全低于或高于零，则相应的影响是显著的15。根据理论2，我们控制了人口统计学（年龄）、功能健康（生理生活质量）和疾病类型（癌症部位；包括在全部样本的所有分析中）。此外，我们对疾病的可见性进行了控制。性别与癌症类型高度混淆（前列腺：所有男性；乳腺：除3例患者外所有女性），因此不包括在内。通过在样本和 报告95%置信区间33。对每个模型进行多重共线性检验（方差膨胀系数≤2.2）。多元离群值，通过马氏距离检测 超过卡方分布临界值且p

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