Abstract. Procrastination at work has been examined relatively scarcely, partly due to the lack of a globally validated and context-specific workplace procrastination scale. This study investigates the psychometric characteristics of the Procrastination at Work Scale (PAWS) among 1,028 office employees from seven countries, namely, Croatia, the Czech Republic, Finland, Slovenia, Turkey, Ukraine, and the United Kingdom. Specifically, it was aimed to test the measurement invariance of the PAWS and explore its discriminant validity by examining its relationships with work engagement and performance. Multi-group confirmatory factor analysis shows that the basic factor structure and item loadings of the PAWS are invariant across countries. Furthermore, the two subdimensions of procrastination at work exhibited different patterns of relationships with work engagement and performance. Whereas soldiering was negatively related to work engagement and task performance, cyberslacking was unrelated to engagement and performance. These results indicate further validity evidence for the PAWS and the psychometric characteristics show invariance across various countries/languages. Moreover, workplace procrastination, especially soldiering, is a problematic behavior that shows negative links with work engagement and performance.
Background It is encouraging to see a substantial increase in individuals surviving cancer. Even more so since most of them will have a positive effect on society by returning to work. However, many cancer survivors have unmet needs, especially when it comes to improving their quality of life (QoL). Only few survivors are able to meet all of the recommendations regarding well-being and there is a body of evidence that cancer survivors’ needs often remain neglected from health policy and national cancer control plans. This increases the impact of inequalities in cancer care and adds a dangerous component to it. The inequalities affect the individual survivor, their career, along with their relatives and society as a whole. The current study will evaluate the impact of the use of big data analytics and artificial intelligence on the self-efficacy of participants following intervention supported by digital tools. The secondary endpoints include evaluation of the impact of patient trajectories (from retrospective data) and patient gathered health data on prediction and improved intervention against possible secondary disease or negative outcomes (e.g. late toxicities, fatal events). Methods/design The study is designed as a single-case experimental prospective study where each individual serves as its own control group with basal measurements obtained at the recruitment and subsequent measurements performed every 6 months during follow ups. The measurement will involve CASE-cancer, Patient Activation Measure and System Usability Scale. The study will involve 160 survivors (80 survivors of Breast Cancer and 80 survivors of Colorectal Cancer) from four countries, Belgium, Latvia, Slovenia, and Spain. The intervention will be implemented via a digital tool (mHealthApplication), collecting objective biomarkers (vital signs) and subjective biomarkers (PROs) with the support of a (embodied) conversational agent. Additionally, the Clinical Decision Support system (CDSS), including visualization of cohorts and trajectories will enable oncologists to personalize treatment for an efficient care plan and follow-up management. Discussion We expect that cancer survivors will significantly increase their self-efficacy following the personalized intervention supported by the m-HealthApplication compared to control measurements at recruitment. We expect to observe improvement in healthy habits, disease self-management and self-perceived QoL. Trial registration ISRCTN97617326. https://doi.org/10.1186/ISRCTN97617326. Original Registration Date: 26/03/2021.
Residential satisfaction questionnaires: A systematic review Residential satisfaction is a topic that has been extensively studied in recent decades because it can offer important insights into the quality of the residential environment. However, many inconsistencies and unanswered questions on this topic still persist. Because the understanding of any field of inquiry is importantly affected by the quality of the methodology and measurement instruments employed, this article explores the current state of development and investigation of the psychometric properties of one of the most widely employed methods of measuring residential satisfaction: self-assessment questionnaires that measure satisfaction by assessing satisfaction with specific aspects of the residential environment. A review of representative studies shows a general lack of properly developed and validated questionnaires, lack of sufficient reporting on the origin, development, and psychometric characteristics of the questionnaires employed, and often too little thought and effort invested in developing and validating questionnaires. Such observations are especially important for evaluating the quality of studies and their implications for residential satisfaction, and they are the points where research practice could be improved.
Background Cancer survivors often experience disorders from the depressive spectrum that remain largely unrecognized and overlooked. Even though screening for depression is recognized as essential, several barriers prevent its successful implementation. It is possible that better screening options can be developed. New possibilities have been opening up with advances in artificial intelligence and increasing knowledge on the connection of observable cues and psychological states. Objective The aim of this scoping meta-review was to identify observable features of depression that can be intercepted using artificial intelligence in order to provide a stepping stone toward better recognition of depression among cancer survivors. Methods We followed a methodological framework for scoping reviews. We searched SCOPUS and Web of Science for relevant papers on the topic, and data were extracted from the papers that met inclusion criteria. We used thematic analysis within 3 predefined categories of depression cues (ie, language, speech, and facial expression cues) to analyze the papers. Results The search yielded 1023 papers, of which 9 met the inclusion criteria. Analysis of their findings resulted in several well-supported cues of depression in language, speech, and facial expression domains, which provides a comprehensive list of observable features that are potentially suited to be intercepted by artificial intelligence for early detection of depression. Conclusions This review provides a synthesis of behavioral features of depression while translating this knowledge into the context of artificial intelligence–supported screening for depression in cancer survivors.
(1) Background: The needs of cancer survivors are often not reflected in practice. One of the main barriers of the use of patient-reported outcomes is associated with data collection and the interpretation of patient-reported outcomes (PROs) due to a multitude of instruments and measuring approaches. The aim of the study was to establish an expert consensus on the relevance and key indicators of quality of life in the clinical practice of breast cancer survivors. (2) Methods: Potential indicators of the quality of life of breast cancer survivors were extracted from the established quality of life models, depicting survivors’ perspectives. The specific domains and subdomains of quality of life were evaluated in a two-stage online Delphi process, including an international and multidisciplinary panel of experts. (3) Results: The first round of the Delphi process was completed by 57 and the second by 37 participants. A consensus was reached for the Physical and Psychological domains, and on eleven subdomains of quality of life. The results were further supported by the additional ranking of importance of the subdomains in the second round. (4) Conclusions: The current findings can serve to optimize the use of instruments and address the challenges related to data collection and interpretation as the facilitators of the adaption in routine practice.
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