Development of Japanese Measures of Reconciliatory Tendencies:The Japanese Trait Forgivingness Scale and the Japanese Proclivity to Apologize Measure
Development of Japanese Measures of Reconciliatory Tendencies:The Japanese Trait Forgivingness Scale and the Japanese Proclivity to Apologize Measure
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日本和解倾向措施的发展：日本特质宽恕量表与日本道歉措施倾向
DOI：
10.34216/2073-1426-2021-27-4-144-154
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Ayano Yagi
中科院分区：
文献类型：
--
作者：
Y. Ohtsubo;Kazuho Yamaura;Ayano Yagi
和解过程受两个重要的倾向性变量的影响：（i）受害者原谅施暴者的倾向性，（ii）施暴者向受害者道歉的倾向性。我们翻译现存的英语措施，这些处置成日语使用回译法。然后，我们研究了两个结果的措施，日本特质遗忘量表（J-TFS）和日本倾向道歉措施（J-PAM）的有效性。与之前的研究结果一致，J-TFS得分与宜人性、神经质（反向）和主观幸福感相关，而J-PAM得分与宜人性和主观幸福感相关。有趣的是，这两种和解倾向彼此呈正相关，即使在控制宜人性的情况下（即，（1）和谐的社会关系。此外，两个自传体回忆研究（宽恕和道歉的实际情况）证实了这两种措施的有效性。J-TFS预测了参与者原谅同事在工作场所犯下的错误的程度，而J-PAM预测了参与者是否在最近的违法行为后向受害者道歉。
Reconciliation processes are in uenced by two important dispositional variables: (i) the victim’s disposition to forgive the o ender, and (ii) the o ender’s disposition to apologize to the victim. We translated extant English measures of each of these dispositions into Japanese using the back-translation method. We then examined the validity of the two resultant measures, the Japanese Trait Forgivingness Scale (J-TFS) and the Japanese Proclivity to Apologize Measure (J-PAM). Consistent with previous ndings, J-TFS scores were correlated with agreeableness, neuroticism (inversely), and subjective well-being, while J-PAM scores were correlated with agreeableness and subjective well-being. Interestingly, these two reconciliatory tendencies were positively correlated with each other, even when controlling for agreeableness (i.e., a preference for harmonious social relationships). In addition, two autobiographical recall studies (of actual instances of forgiveness and apology) con rmed the validity of these two measures. e J-TFS predicted the extent to which participants had forgiven a workplace o ense in icted by one of their co-workers, while the J-PAM predicted whether participants had apologized to their victims following a recent transgression.
(2).利用机器学习从GPCR-肽相互作用预测器中提取相互作用机制
DOI：
--
发表时间：
2019
期刊：
影响因子：
--
作者：
白石慧 佐竹炎
通讯作者：
白石慧 佐竹炎
影响因子：
7.6
作者：
McCullough, ME;Rachal, KC;Hight, TL
通讯作者：
Hight, TL