Publications by authors named "Elke Veirman"

Article Synopsis
  • Big data and machine learning can help us understand how psychological factors affect pain, but we need really good data to do this correctly.
  • There are challenges with current evidence about how these factors influence chronic pain, so improvements are needed.
  • Researchers should be clearer about how they think causes and effects work, which can help design better studies and lead to more useful results.
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Principal Component Metrics is a novel theoretically-based and data-driven methodology that enables the evaluation of the internal structure at item level of maximum emotional intelligence tests. This method disentangles interindividual differences in emotional ability from acquiescent and extreme responding. Principal Component Metrics are applied to existing (Mayer-Salovey-Caruso Emotional Intelligence Test) and assembled (specifically, the Situational Test of Emotion Understanding, the Situational Test of Emotion Management, and the Geneva Emotion Recognition Test) emotional intelligence test batteries in an analysis of three samples (total = 2,303 participants).

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Introduction: To limit the spread of COVID-19, many countries, including Belgium, have installed physical distancing measures. Yet, adherence to these newly installed behavioral measures has been described as challenging and effortful. Based on the Health Action Process Approach (HAPA) model, this study performed an in-depth evaluation of when, why, and how people deviated from the physical distancing measures.

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Background: Questions have been raised about whether items of alexithymia scales assess the construct alexithymia and its key features, and no other related constructs. This study assessed the (discriminant) content validity of the most widely used alexithymia scale, i.e.

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Background: Fibromyalgia is characterized by widespread musculoskeletal pain and often accompanied by cognitive and emotional problems. Adaptation to fibromyalgia may therefore also rely on one's ability to regulate emotional problems. In this study, we examined two indices of emotion regulation, that is, (a) affective instability, involving frequent large fluctuations in self-reported affect, and (b) resting heart rate variability (HRV).

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Background: The COVID-19 pandemic requires massive and rapid behavior change. The Health Action Process Approach (HAPA) describes personal determinants that play a key role in behavior change. This study investigated whether these determinants are associated with adherence to physical distancing measures to prevent the spread of COVID-19 (i.

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Background: Concerns have been raised about whether self-report measures of pain catastrophizing reflect the construct as defined in the cognitive-behavioral literature. We investigated the content of these self-report measures; that is, whether items assess the construct 'pain catastrophizing' and not other theoretical constructs (i.e.

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Screening tools allowing to predict poor pain outcomes are widely used. Often these screening tools contain psychosocial risk factors. This review (1) identifies multidimensional screening tools that include psychosocial risk factors for the development or maintenance of pain, pain-related distress, and pain-related disability across pain problems in adults, (2) evaluates the quality of the validation studies using Prediction model Risk Of Bias ASsessment Tool (PROBAST), and (3) synthesizes methodological concerns.

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The Levels of Emotional Awareness Scale for Children (LEAS-C) is a performance-based instrument that assesses emotional awareness in the structure of written responses to a set of real-life scenarios. While it is theoretically expected that emotional awareness develops with age, virtually no age differences have been established with the LEAS-C. The present study investigated whether an adaptation of the instructions and scoring procedure on the basis of the componential emotion approach could improve the validity of the LEAS-C and reveal the theoretically expected age differences.

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Recent research has claimed that a novelty dimension is needed to represent the cognitive emotion structure over and above valence, power and arousal. Novelty emerged when student samples evaluated the meaning of 24 emotion terms on 142 emotion features. This claim is debatable, however, because to date novelty has never been found in similarity sorting studies.

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