Driver conflict response during supervised automation: Do hands on wheel matter?

Securing appropriate driver responses to conflicts is essential in automation that is not perfect (because the driver is needed as a fall-back for system limitations and failures). However, this is recognized as a major challenge in the human factors literature. Moreover, in-depth knowledge is lacking regarding mechanisms affecting the driver response process. The first aim of this study was to investigate how driver conflict response while using highly reliable (but not perfect) supervised automation differ for drivers that (a) crash or avoid a conflict object and (b) report high trust or low trust in automation to avoid the conflict object. The second aim was to understand the influence on the driver conflict response of two specific factors: a hands-on-wheel requirement (with vs. without), and the conflict object type (garbage bag vs. stationary vehicle). Seventy-six participants drove with highly reliable but supervised automation for 30 min on a test track. Thereafter they needed to avoid a static object that was revealed by a lead-vehicle cut-out. The driver conflict response was assessed through the response process: timepoints for driver surprise reaction, hands-on-wheel, driver steering, and driver braking. Crashers generally responded later in all actions of the response process compared to non-crashers. In fact, some crashers collided with the conflict object without even putting their hands on the wheel. Driver conflict response was independent of the hands-on-wheel requirement. High-trust drivers generally responded later than the low-trust drivers or not at all, and only high trust drivers crashed. The larger stationary vehicle triggered an earlier surprise reaction compared to the garbage bag, while hands-on-wheel and steering response were similar for the two conflict object types. To conclude, crashing is associated with a delay in all actions of the response process. In addition, driver conflict response does not change with a hands-on-wheel requirement but changes with trust-level and conflict object type. Simply holding the hands on the wheel is not sufficient to prevent collisions or elicit earlier responses. High trust in automation is associated with late response and crashing, whereas low trust is associated with appropriate driver response. A larger conflict object trigger earlier surprise reactions.     

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.     

FN400和LPC对熟悉性和回想的特异性指向分析

双加工理论认为熟悉性和回想是情景记忆的两个独立过程,其重要证据是ERP研究中FN400和LPC可以分别指示熟悉性和回想。但也有研究证明FN400是概念启动和熟悉性共同作用的结果,而LPC也不能准确的反映回想加工,因此,将其作为双加工理论证据的可靠性需进一步讨论。未来需进一步明确概念启动和熟悉性影响FN400的前提条件,并在提高生态效度和控制额外变量的同时明晰LPC的指向性,同时探究熟悉性和回想在再认过程中的具体作用方式。     

The stranded cinderella and the wandering rascal. Two narratives of female housing exclusion

This article examines the life stories of two young Polish women who have faced severe marginalization and homelessness. The main aim of this article is to investigate how the notions of (in)visibility and (im)mobility are shaping participants' everyday lives and life stories they tell. Using concepts drawn from social sciences, as well as cultural and literary studies, this article attempts to reconstruct the narrative frameworks of their life stories. Marzena's story is a non-linear collection of adventures reminiscent of a picaresque novel, but with fractured agency, which leaves her powerless and restlessly moving around the city. Angelika, on the other hand, is trapped in a two-room dilapidated house with no running water, located in a rural part of Poland. Her narrative resembles a telenovela with melodramatic plot twists, romance and hard-to-believe revelations about the narrator's true self; further, it is a purposeful journey in search for happiness and fortune. Surprisingly, Marzena's mobility makes it unlikely for her to overcome homelessness, while the stranded Angelika finds the resilience necessary to improve her life. The proposed analysis contributes to a deeper understanding of women's experiences of homelessness and demonstrates the need to regard housing exclusion also as a biographical process.     

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.     

Explaining the Asian American Youth Paradox: Universal Factors versus Asian American Family Process Among Filipino and Korean American Youth

This study used longitudinal survey data of Filipino American and Korean American youth to examine ways in which universal factors (e.g., peer antisocial behaviors and parent–child conflict) and Asian American (AA) family process variables (e.g., gendered norms) independently and collectively predict grade point average (GPA), externalizing, and internalizing problems. We aimed to explain the “Asian American youth paradox” in which low externalizing problems and high GPA coexist with high internalizing problems. We found that universal factors were extensively predictive of youth problems and remained robust when AA family process was accounted for. AA family process also independently explained youth development and, in part, the AA youth paradox. For example, gendered norms increased mental distress. Academic controls did the opposite of what it is intended, that is, had a negative impact on GPA as well as other developmental domains. Family obligation, assessed by family-centered activities and helping out, was beneficial to both externalizing and internalizing youth outcomes. Parental implicit affection, one of the distinct traits of AA parenting, was beneficial, particularly for GPA. This study provided important empirical evidence that can guide cross-cultural parenting and meaningfully inform intervention programs for AA youth.