ADHD Strengths, Challenges and Adaptation in Employment - A Systematic Review

Approximately 4% of the labour force has a diagnosis of ADHD (de Graaf et al., 2008). Although ADHD was historically construed as a disorder of childhood, many studies find that the effects of ADHD (or ADHD in remission) persist well into adulthood (Faraone et al., 2006). Therefore, there is a pressing need for researchers and clinicians alike to better understand how these persisting symptoms affect important areas of adult life functioning such as employment. The current systematic review aims to detail the experience, challenges, benefits and adaptations of individuals with ADHD in the workplace. We report the effects of ADHD on employment outcomes, including but not limited to workplace challenges, workplace performance, job satisfaction, interpersonal relationships at work, maladaptive work thoughts and behaviours, personal strengths, ADHD empowerment, person-environment fit and accommodations and support and sex differences

Transdiagnostic Cognitive Biases in Psychiatric Disorders

Systematic review and meta-analysis on the presence and specificity of cognitive biases in transdiagnostic psychiatric disorder

Transdiagnostic cognitive biases in psychiatric disorders: A systematic review and network meta-analysis

Psychiatric disorders are characterized by cognitive deficits, which have been proposed as a transdiagnostic feature of psychopathology (“C” factor). Similarly, cognitive biases (e.g., in attention, memory, and interpretation) represent common tendencies in information processing that are often associated with psychiatric symptoms. However, the question remains whether cognitive biases are also transdiagnostic or are specific to certain psychiatric disorders/symptoms. The current systematic review (osf.io/znf4q) sought to address whether the proposed “C” factor of transdiagnostic cognitive dysfunction in psychopathology can be extended to cognitive biases. Overall, 31 studies comprising 4401 participants (2536 patients, 1865 non-clinical controls) across 21 diagnostic categories met inclusion criteria, assessing 19 cognitive biases with most studies focusing on interpretation (k = 22) and attention (k = 11) biases, with only 2 assessing memory biases. Traditional meta-analyses found a moderate effect size (g = 0.32) for more severe cognitive biases in all patients relative to non-clinical controls, as well as small but significant associations between interpretation biases and transdiagnostic symptom categories (general psychopathology: r = .20, emotion dysfunction: r = 0.17, psychotic symptoms: r = 0.25). Network meta-analyses revealed significant patient versus control differences on attention and interpretation biases across diagnoses, as well as significant differences between diagnoses, with highest severity in panic disorder for attention biases and obsessive-compulsive disorder for interpretation biases. The current findings support a big “C” interpretation of transdiagnostic cognitive dysfunction in psychiatric disorders, extending the concept to cognitive biases and transdiagnostic symptom dimensions. They also suggest that while the presence of cognitive biases is transdiagnostic, bias severity differs across diagnoses, as in traditional neurocognitive deficits

An intersectional perspective on the sociodemographic and clinical factors influencing the status of Not in Education, Employment, or Training (NEET) in patients with first-episode psychosis (FEP)

Following the onset of a First Episode of Psychosis (FEP), the rate of individuals Not in Education, Employment, or Training (NEET) appears to drastically increase. Sociodemographic and clinical factors were reported to be associated with NEET status in FEP patients. This study examined how these sociodemographic and clinical factors were linked to NEET status in FEP patients independently and from an intersectional perspective. It was hypothesized that NEET status in FEP patients would be described by the intersection between at least two predictor variables. Secondary analyses were conducted on files of participants recruited from a local FEP clinic. Univariate logistic regression and Chi-squared Automatic Interaction Detection (CHAID) analyses were performed on a total of 440 participants with FEP. Univariate logistic regressions indicated that age (p = .03), socioeconomic status (p < .001), and negative symptom severity (p < .001) were significant independent predictors of NEET. CHAID analyses suggested an intersectional pattern of negative symptom severity and socioeconomic status in differentiating between FEP patients with NEET versus non-NEET status. The applicability and generalizability of results from this study were enhanced by the large and representative sample as well as the use of benchmark quantitative intersectionality research methods. Future intersectionality research on NEET with a clinical population is needed to validate and expand the current results by including more sociodemographic variables