Although the attention for neurodiversity in human resource management (HRM) is growing, neurodivergent individuals are still primarily supported from a deficit-oriented paradigm, which points towards individuals' deviation from neurotypical norms. Following the HRM process model, our study explored to what extent a strengths-based HRM approach to the identification, use, and development of strengths of neurodivergent groups is intended, implemented, and perceived in organizations. Thirty participants were interviewed, including HRM professionals (n=15), supervisors of neurodivergent employees (n=4), and neurodivergent employees (n=11). Our findings show that there is significant potential in embracing the strengths-based approach to promote neurodiversity-inclusion, for instance with the use of job crafting practices or (awareness) training to promote strengths use. Still, the acknowledgement of neurodivergent individuals' strengths in the workplace depends on the integration of the strengths-based approach into a supportive framework of HR practices related to strengths identification, use, and development. Here, particular attention should be dedicated to strengths development for neurodivergent employees (e.g., optimally balancing strengths use). By adopting the strengths-based HRM approach to neurodiversity as a means of challenging the ableist norms of organizations, we add to the HRM literature by contributing to the discussion on how both research and organizations can optimally support an increasingly diverse workforce by focusing on individual strengths
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This research concerning the experience and future of zoos was carried out from 2011-2012 and takes regional ideas concerning Zoo Emmen as well as global visions into account. The research focuses partly on Zoo Emmen, its present attractions and visitors while also comparing and contrasting visions on the future in relationship to other international zoos in the world. In this way, remarkable experiences and ideas will be identified and in the light of them, it can serve as inspiration for stakeholders of zoos at large. The main research subject is a look at the future zoos in view of: The Zoo Experience – an international experience benchmark; The Zoo of the Future – a Scenario Planning approach towards the future; The virtual zoo - zoo’s in the internet domain.
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The prevention and diagnosis of frailty syndrome (FS) in cardiac patients requires innovative systems to support medical personnel, patient adherence, and self-care behavior. To do so, modern medicine uses a supervised machine learning approach (ML) to study the psychosocial domains of frailty in cardiac patients with heart failure (HF). This study aimed to determine the absolute and relative diagnostic importance of the individual components of the Tilburg Frailty Indicator (TFI) questionnaire in patients with HF. An exploratory analysis was performed using machine learning algorithms and the permutation method to determine the absolute importance of frailty components in HF. Based on the TFI data, which contain physical and psychosocial components, machine learning models were built based on three algorithms: a decision tree, a random decision forest, and the AdaBoost Models classifier. The absolute weights were used to make pairwise comparisons between the variables and obtain relative diagnostic importance. The analysis of HF patients’ responses showed that the psychological variable TFI20 diagnosing low mood was more diagnostically important than the variables from the physical domain: lack of strength in the hands and physical fatigue. The psychological variable TFI21 linked with agitation and irritability was diagnostically more important than all three physical variables considered: walking difficulties, lack of hand strength, and physical fatigue. In the case of the two remaining variables from the psychological domain (TFI19, TFI22), and for all variables from the social domain, the results do not allow for the rejection of the null hypothesis. From a long-term perspective, the ML based frailty approach can support healthcare professionals, including psychologists and social workers, in drawing their attention to the nonphysical origins of HF.
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