1,720,979 research outputs found
Investigating Emotional Contagion in Counselling Practitioners
No full textEmotional contagion theory proposes that people have a natural tendency to copy the expressions, behaviours and vocalisations of other people in their everyday interactions and as a consequence, come to feel, if not the same emotion, one that is
congruent to that being felt by the person(s) being observed (Hatfield et al., 1994). Previous empirical research has found that emotional contagion holds key relevance for counselling practitioners and their clients. Clients experiencing empathy are
reported to experience a feeling of being deeply understood, more prepared to explore their feelings and show an increased satisfaction with therapy. However, potential negative aspects of emotional contagion also exist. Practitioners may feel emotionally overwhelmed by the persistent transfer of clients’ emotions, potentially leading to depersonalisation, detachment, compassion fatigue and/or burnout.
The current research investigated how much of the sharing of emotions within the therapeutic relationship can be attributed to emotional contagion in order that practitioners become better resourced to facilitate the processes that lead to empathy
and also ameliorate any negative impact through the modulation of emotional contagion and engagement of emotion regulation. A series of five mixed-methods studies were conducted. The applicability of emotion contagion theory in counselling
settings was tested and investigated from the perspective of both practitioners and former counselling clients using pre-validated measures and a new 55-item measurement tool, Emotion Contagion in Counselling Scale.
Results showed that emotional contagion does occur in this target population. 57% of practitioners’ reporting feeling clients’ emotions physically often. Emotional contagion was reported to enhance the sense of connection and resonance with
clients and appears to facilitate relational depth. However, in prioritising attention on the client’s emotional experiencing, and bracketing and suppressing their own emotional response, counsellors somatised. When this somatised emotion remains
unprocessed and/or undischarged, practitioners experience significant adverse effects including intrusive thoughts, feeling burdened, unable to settle and occasionally experiencing a residue from their work.
Hence, as well as paying careful attention to the impact of aspects of transferred emotion from clients which have been consciously experienced, practitioners need to become vigilant for any residual emotion and endeavour to ensure that it is either processed and/or discharged. It is advocated that practitioners are made aware of the process of emotion contagion and its underlying mechanisms. Practitioners are advised to regularly self-monitor for this emotional residue and proactively use strategies to discharge unprocessed emotion regularly. Initial validation of a new measure, Emotion Contagion in Counselling Scale is reported. With future studies the new measure can be further validated and made available for practitioners for use after sessions and in Supervision
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Side preferences in human dyads when walking: the influence of country, threat, handedness, and sex
Full text link© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.In several species, lateral position preferences have been observed in pair mates, mother–infant dyads, and during agonistic interactions. This research examined side preferences in human dyads in an observational study and survey. We observed 1236 male–female pairs walking in the UK and found a bias for males to walk on the right side of the pair, which did not depend on hand-holding, or walking during daylight or darkness. The survey measured side preferences in 798 participants (398 left-handed, 411 right-handed), from the UK (402) and USA (396). Participants chose a side to walk when walking with their partner, or alone, in various threatening/non-threatening scenes. Threat did not influence preference in walking couples, but males, when passing a threatening stranger, preferred the best combat side for their handedness. Country and handedness also influenced preferences. Left-handers preferred the left side and right-handers preferred the right side, and USA participants exhibited a more rightward preference than UK participants. The pattern of preference for each country was equivalent, showing independent influences of handedness and cultural learning. Overall, the results suggest that males and females prefer the side that allows their dominant hand to be on the outside of the dyad.The authors gratefully acknowledge the funding for Study 2, which was provided by an internal University of Chester grant with the grant code QR737
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Initial validation of the general attitudes towards Artificial Intelligence Scale
Full text linkAuthor Accepted Manuscript with Appendix A (Sources of News Stories) and Appendix B (General Attitudes Towards Artificial Intelligence Scale, with instructions and scoring). For data files, please follow the DOI https://doi.org/10.1016/j.chbr.2020.100014 to the publisher's site. This article is available Open Access via the Publisher's site: https://www.sciencedirect.com/science/article/pii/S2451958820300142A new General Attitudes towards Artificial Intelligence Scale (GAAIS) was developed. The scale underwent initial statistical validation via Exploratory Factor Analysis, which identified positive and negative subscales. Both subscales captured emotions in line with their valence. In addition, the positive subscale reflected societal and personal utility, whereas the negative subscale reflected concerns. The scale showed good psychometric indices and convergent and discriminant validity against existing measures. To cross-validate general attitudes with attitudes towards specific instances of AI applications, summaries of tasks accomplished by specific applications of Artificial Intelligence were sourced from newspaper articles. These were rated for comfortableness and perceived capability. Comfortableness with specific applications was a strong predictor of general attitudes as measured by the GAAIS, but perceived capability was a weaker predictor. Participants viewed AI applications involving big data (e.g. astronomy, law, pharmacology) positively, but viewed applications for tasks involving human judgement, (e.g. medical treatment, psychological counselling) negatively. Applications with a strong ethical dimension led to stronger discomfort than their rated capabilities would predict. The survey data suggested that people held mixed views of AI. The initially validated two-factor GAAIS to measure General Attitudes towards Artificial Intelligence is included in the Appendix
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
The Impact of Artificial Intelligence on Societies: Understanding Attitude Formation Towards AI
Full text linkThis is an Author Accepted Manuscript version of the following chapter: Schepman, A. & Rodway, P., The measurement of attitudes towards Artificial Intelligence: An overview and recommendations, published in The Impact of AI on societies: Understanding attitude formation towards AI, edited by Montag, C. & Ali, R. (Eds.) 2025, Springer, reproduced with permission of Springer. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-031-70355-3_2The growth in use of Artificial Intelligence is having a major impact on society, with further impacts anticipated in the coming years and decades. There are individual differences in attitudes towards Artificial Intelligence and it is important for scientists and others to be able to measure these. Individual differences in attitudes towards Artificial Intelligence may be associated with other major psychological or circumstantial factors, and understanding these associations is beneficial. In addition, it is important to be able to track attitudes towards Artificial Intelligence over time. For this purpose, scientists have developed psychometric measurement tools to measure attitudes towards Artificial Intelligence. This Chapter provides an overview and evaluation of these tools, with a focus on tools that measure general attitudes towards Artificial Intelligence, and that are quantitative measurements, which can be analysed statistically. Semantic, methodological, and psychometric factors that the user should consider when choosing a suitable tool are discussed. The choice of measurement tool may depend on many researcher-driven considerations, including time, cost, and practical factors, but the quality and validity of the measurement tool should be a major factor in this choice. A scale’s ability to capture important dimensions in the data should also be a key consideration. We recommend that observed ambivalence about AI is best captured with a bi-dimensional AI attitudes scale.Unfunde
Validation of the Short General Attitudes Towards Artificial Intelligence Scale: The Short GAAIS-10
Full text link© 2026 The Author(s). Published with license by Taylor & Francis Group, LLC.With Artificial Intelligence becoming ever more widespread, it is important to have instruments that measure people’s attitudes toward Artificial Intelligence efficiently. Providing such a tool was the aim of the current report. Its authors report the validation of a shortened and further purified version of General Attitudes toward Artificial Intelligence Scale (GAAIS) consisting of ten items (Short GAAIS-10) based on empirical survey data from UK-based participants, collected online (total N = 1406). The multi-phase study design was based on Factor Analysis, Item Response Theory, correlation, and multivariate multiple regression. Phase 1 was a rigorous selection phase based on three independent prior samples of approximately 300 participants each (collected in 2021 and 2022). Confirmatory Factor Analysis (CFA) and Polytomous Rasch Analysis (PTA) alongside semantic judgements were used to select 10 items. In phase 2, a new representative UK sample (N = 500) was drawn in 2024 to further validate the Short GAAIS-10. CFA and PTA of the new data revealed good psychometric properties of the Short GAAIS-10, which is bidimensional with two subscales (Positive, Negative). The Short GAAIS-10 showed predictive validity against the Technology Readiness Index based on correlation analysis. Rated comfortableness with seven types of AI applications was positively predicted by the Short GAAIS-10 subscales based on multivariate multiple regression. The Short GAAIS-10 is a valid and streamlined instrument with which to measure General Attitudes toward Artificial Intelligence. The ambivalent nature of public AI attitudes is discussed as an important observation for a general AI attitude scale to capture. The contribution of this research is the creation of a short version of a valid and efficient psychometric instrument with which to measure general attitudes toward Artificial Intelligence.This work was supported by the University of Chester under [Grant QR738]
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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