Development and validation of iranian children's participation assessment scale

Background: Participation is mostly cultural and familial based, and there is not any assessment scales for evaluating kids' participation in Iranian context, therefore the purpose of this study was developing children's participation assessment scale for Iranian children. Methods: Development of this scale occurred in two phases; phase I: planning: following reviewing the literature and adopting and compiling some items of available evaluation tools in the area (such as CAPE, CPQ, CLASS, Life-H) and receiving advice from two expert panels, the preliminary94- item questionnaire was prepared. Phase II: construct: the survey study was carried out on40 children and 21 of their parents to assess the popularity of the activity in Iran; thus, the items of the questionnaire reduced to 92 and after face and content validity, the final version prepared with 71 items. Results: The final 71-item questionnaire was developed in two parent-report and child-report versions. The 71 items based on the literature and expert panels' advice were categorized in 8 areas of occupation according to Occupational Therapy Practice Framework (ADL, IADL, Play, leisure, social participation, education, work, and sleep/rest). Conclusion: Iranian children's participation assessment is a useful and culturally relevant tool to measure participation of Iranian children. It can be used in rigorous clinical and population-based research

Does workplace spirituality enhance motivation of hospitals social workers? The scrutiny in Iran

Background: In today's world as the globe of organizations, employees tend to show endeavor and more involvement in organizational goals and mission by creating workplace spirituality. Aim: The present study aimed to investigate the role of workplace spirituality in improving hospital social worker's motivation. Methods: The study employed a cross-sectional design and was conducted in 19 private and governmental hospitals in Karaj city, Alborz province, north Iran in 2019. The statistical population included all 302 hospital social workers who were selected as the sample population according to Cochran's formula. Using two standard questionnaires of workplace spirituality and employee motivation, we assessed the role of workplace spirituality in improving the hospital' social workers' motivation. Results: According to research findings, there were significant relationships between workplace spirituality, meaningful working, sense of community, forgiveness, and honesty with hospital social workers motivation p <= .05. There was a significant difference between age group, gender, level of education and workplace spirituality, and motivation of hospital social workers p <= .05. Conclusion: Findings suggest that health planners and authorities may need to examine factors that contribute to the promotion of workplace spirituality to increase the motivation of hospital social workers for improved performance of health organizations

Objective evaluation of deep uncertainty predictions for COVID-19 detection

Deep neural networks (DNNs) have been widely applied for detecting COVID-19 in medical images. Existing studies mainly apply transfer learning and other data representation strategies to generate accurate point estimates. The generalization power of these networks is always questionable due to being developed using small datasets and failing to report their predictive confidence. Quantifying uncertainties associated with DNN predictions is a prerequisite for their trusted deployment in medical settings. Here we apply and evaluate three uncertainty quantification techniques for COVID-19 detection using chest X-Ray (CXR) images. The novel concept of uncertainty confusion matrix is proposed and new performance metrics for the objective evaluation of uncertainty estimates are introduced. Through comprehensive experiments, it is shown that networks pertained on CXR images outperform networks pretrained on natural image datasets such as ImageNet. Qualitatively and quantitatively evaluations also reveal that the predictive uncertainty estimates are statistically higher for erroneous predictions than correct predictions. Accordingly, uncertainty quantification methods are capable of flagging risky predictions with high uncertainty estimates. We also observe that ensemble methods more reliably capture uncertainties during the inference. DNN-based solutions for COVID-19 detection have been mainly proposed without any principled mechanism for risk mitigation. Previous studies have mainly focused on on generating single-valued predictions using pretrained DNNs. In this paper, we comprehensively apply and comparatively evaluate three uncertainty quantification techniques for COVID-19 detection using chest X-Ray images. The novel concept of uncertainty confusion matrix is proposed and new performance metrics for the objective evaluation of uncertainty estimates are introduced for the first time. Using these new uncertainty performance metrics, we quantitatively demonstrate when we could trust DNN predictions for COVID-19 detection from chest X-rays. It is important to note the proposed novel uncertainty evaluation metrics are generic and could be applied for evaluation of probabilistic forecasts in all classification problems