Research in counselling and psychotherapy Post‐COVID‐19

The COVID‐19 pandemic brings to light many areas the field of counselling and psychotherapy may need to address in future research. We outline several issues stemming from or exacerbated by the pandemic and offer suggestions for future research to address the mental health needs of those impacted. Our suggestions focus on five domains: (a) the health and well‐being of helping professionals, (b) the infodemic, (c) discrimination and minority stress, (d) spiritual and existential dynamics in mental health and (e) couple and family stress and resilience. We aim to provide a multi‐systemic perspective of mental health and well‐being in the time of COVID‐19, as well as encourage current and future studies to incorporate these suggestions to advance the health and well‐being of our communities through evidence‐based treatment approaches.     

Gerontological Counseling Trends in Adultspan Journal: 1999–2019

Using quantitative content analysis, we categorized 76 out of 154 articles (49.35%) published in Adultspan Journal from 1999 to 2019 as focused on gerontological counseling. This study explored publication trends for these articles and compared results with a larger study (Fullen et al., 2019) on publication trends for gerontological counseling across all counseling‐related journals.     

A systematic review of the effectiveness of machine learning for predicting psychosocial outcomes in acquired brain injury: Which algorithms are used and why?

Clinicians working in the field of acquired brain injury (ABI, an injury to the brain sustained after birth) are challenged to develop suitable care pathways for an individual client’s needs. Being able to predict psychosocial outcomes after ABI would enable clinicians and service providers to make advance decisions and better tailor care plans. Machine learning (ML, a predictive method from the field of artificial intelligence) is increasingly used for predicting ABI outcomes. This review aimed to examine the efficacy of using ML to make psychosocial predictions in ABI, evaluate the methodological quality of studies, and understand researchers’ rationale for their choice of ML algorithms. Nine studies were reviewed from five databases, predicting a range of psychosocial outcomes from stroke, traumatic brain injury, and concussion. Eleven types of ML were employed with a total of 75 ML models. Every model was evaluated as having high risk of bias, unable to provide adequate evidence for predictive performance due to poor methodological quality. Overall, there was limited rationale for the choice of ML algorithms and poor evaluation of the methodological limitations by study authors. Considerations for overcoming methodological shortcomings are discussed, along with suggestions for assessing the suitability of data and suitability of ML algorithms for different ABI research questions.     

The Nature of Client Agency Prior to Therapy: A Qualitative Study on Clients’ Narratives

Client agency is considered a crucial contributor to good treatment outcome. Recent studies, however, differ strongly in how they conceptualise and investigate agency. The current study explores the nature of client agency in ten clients’ pre-treatment interviews. Applying Consensual Qualitative Research, we constructed three overarching categories, subdivided into 14 sub-categories capturing both between- and within-person differences in agency before therapy. We found that all participants oscillated between the experience of a lack of grip on problems on the one hand and noticing their involvement in the problem and taking action on the other. These results present a dynamic conceptualisation of client agency. This allows us to ask pertinent questions for both future research and clinical practice.     

Does inconsistency always lead to negative affect? The influence of need for closure on affective reactions to cognitive inconsistency

We present an experiment showing that need for closure (NFC)—defined as the epistemic desire for certainty—can moderate individuals' affective reactions to cognitive inconsistency. Informed by Kruglanski and colleagues' new theory, that cognitive inconsistency elicits negative affect particularly under certain circumstances, we find that NFC (i.e. the desire for certain, stable and unambiguous knowledge) influences the strength of consistency effects and resulting negative affect. More specifically, we find that individuals who are high on NFC experience more negative affect upon encountering an inconsistent (vs. consistent) cognition. However, when individuals are low on NFC, inconsistency is irrelevant, and their affect depends on whether the ultimate outcome of the cognition is positive or negative. We discuss the theoretical and practical implications of this research.     

The (Dis)Pleasures of Creativity: Spontaneous Eye Blink Rate during Divergent and Convergent Thinking Depends on Individual Differences in Positive and Negative Affect

Previous research has demonstrated that individual differences in affect and motivation predict divergent and convergent thinking performance, two thinking processes involved in creative idea generation. Individual differences in affect and motivation also predict spontaneous eye blink rate (sEBR) during divergent and convergent thinking; and sEBR predicts divergent and convergent thinking performance. This study investigates experimentally whether the relationship between sEBR and divergent and convergent thinking depends on individual differences in affect and motivation. Eighty-two participants completed the Emotion/motivation-related Divergent and Convergent thinking styles Scale (EDICOS; G. Soroa et al., 2015), performed the alternative uses task (AUT; divergent thinking) or the remote associates task (RAT; convergent thinking), while their sEBR was captured with an eye-tracker. The results showed that individual differences in positive affect positively correlated with sEBR for the AUT, whereas individual differences in negative affect positively correlated with sEBR for the RAT. Furthermore, the interaction between individual differences in positive and negative affect and sEBR predicted divergent and convergent thinking performance. The contribution of our study is therefore that individual differences in positive and negative affect can both positively correlate with sEBR during divergent and convergent thinking; and that this predicts divergent and convergent thinking performance.