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This study aims to modify the human factors analysis and classification system (HFACS) to make it suitable for collapse accident analysis in construction. Based upon the modified HFACS, distribution patterns of causal factors across multiple levels were discerned among causal factors of various stakeholders at construction sites. It explored the correlations between two causal factors from different levels and further determined causation paths from two perspectives of level and stakeholder.

The main research framework consisted of data collection, coding and analysis. Collapse accident reports were collected with adequate causation information. The modified HFACS was utilized for coding causal factors across all five levels in each case. A hybrid approach with two perspectives of level and stakeholder was proposed for frequency analysis, correlation analysis and path identification between causal factors.

Eight causal factors from external organizations at the fifth level were added to the original HFACS. Level-based correlation analyses and path identification provided safety managers with a holistic view of inter-connected causal factors across five levels. Stakeholder-based correlation analyses between causal factors from the fifth level and its non-adjacent levels were implemented based on client, government and third parties. These identified paths were useful for different stakeholders to develop specific safety plans for avoiding construction collapse accidents.

This paper fulfils an identified need to modify and utilize the HFACS model for correlation analysis and path identification of causal factors resulting in collapse accidents, which can provide opportunities for tailoring preventive and protective measures at construction sites.

The purpose of the present research is to address the issue by conceptualizing artificial intelligence (AI) experience quality and its dimensions, and furthermore, to empirically test the relationships among AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention.

The data were collected from an AI community canteen in Shanghai. They were also analyzed using exploratory and confirmatory factor analyses (EFA and CFA) and structural equation modeling (SEM).

Four primary dimensions and 15 sub-dimensions of AI experience quality for community canteens were identified. The hypothesized paths between the higher-order constructs – AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention – were confirmed as well.

This is the first study to synthesize AI experience quality, positive affective reactions, AI experience satisfaction and AI-seeking intention in an AI restaurant setting.

Bipolar disorder is associated with poor mental and physical health outcomes, and therefore, it is crucial to research and develop effective interventions for this population (Grande et al., 2016). Unfortunately, research on the efficacy of current interventions shows only small improvements in symptoms and quality of life (Oud et al., 2016). Additionally, individuals with bipolar disorder face barriers to accessing care like social stigma, isolation and financial constraints (Blixen et al., 2016). This paper aims to introduce and examine the effectiveness of an accessible, peer-led group program, Mood Lifters (Votta and Deldin, 2022), in those who completed the program and also self-reported a diagnosis of bipolar disorder.

Mood Lifters is a 15-week, peer-led group program that approaches mental wellness from a biopsychosocial framework using strategies from a variety of evidence-based treatment methods (e.g. cognitive-behavioral therapy, dialectical behavior therapy, interpersonal psychotherapy, etc.). Participants meet once a week for 1 hour to review various mental health topics, including behavioral changes and insight into mood patterns. Individuals who participated in nonacademic groups in a company setting and self-reported a bipolar diagnosis were surveyed at the beginning and end of their program to measure various aspects of psychological functioning.

Results suggest that these individuals experienced significant improvements in depression, anxiety, social functioning and perceived stress, along with flourishing and positive and negative affect.

These findings are promising, given that bipolar disorder is historically difficult to treat (Grande et al., 2016). Based on this preliminary evidence, the authors have developed a Mood Lifters program specifically for individuals with bipolar disorder and are launching a randomized control clinical trial.