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Cited by 363

The DOI matches its Crossref record; 84 of the 84 references it cites were found in a registry or an index.

A cognitive-emotional model of NSSI: using emotion regulation and cognitive processes to explain why people self-injure

Hasking, Penelope, Whitlock, Janis, Voon, David, Rose, Alyssa·2017·Cognition and Emotion

Abstract

Non-suicidal self-injury (NSSI) is a complex behaviour, routinely engaged for emotion regulatory purposes. As such, a number of theoretical accounts regarding the aetiology and maintenance of NSSI are grounded in models of emotion regulation; the role that cognition plays in the behaviour is less well known. In this paper, we summarise four models of emotion regulation that have repeatedly been related to NSSI and identify the core components across them. We then draw on social cognitive theory to unite models of cognition and models of emotion in developing a new cognitive-emotional model of NSSI. Our model articulates how emotion regulation and cognition can work in concert to govern NSSI, and offers several new research questions that can be addressed within this framework.

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Metadata

Authors
Hasking, P., Whitlock, J., Voon, D., & Rose, A.
Venue
Cognition and Emotion
Published
2016-10-05
Volume
31
Issue
8
Pages
1543-1556
ISSN
  • 0269-9931 (ISSN-L)
  • 1464-0600
OpenAlex
W2528023122
Cited by
363
References
84
FWCI
14.16
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A cognitive-emotional model of NSSI: using emotion regulation and cognitive processes to explain why people self-injure. https://app.cochat.ai/papers/10.1080/02699931.2016.1241219
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Hasking, P., Whitlock, J., Voon, D., & Rose, A. (2017). A cognitive-emotional model of NSSI: using emotion regulation and cognitive processes to explain why people self-injure. Cognition and Emotion. https://doi.org/10.1080/02699931.2016.1241219

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.