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Introduction: Self-managing organizations are a novel organizational form that radically decentralizes decision authority to adapt to the volatile business environment and the demands of knowledge work, resulting in new resources and demands for the employees. Therefore, building on the job demands-resources theory and the person-environment fit theory, the associations of self-managing organizations with higher perceived individual autonomy were tested. Additionally, the study investigated how job crafting and handling mistakes related to the relationship between job autonomy and work engagement/satisfaction., Method: A cross-sectional study was conducted to gather data from employees of different self-managing organizations and non-self-managing organizations, and group comparisons and path analyses were applied to test the preregistered hypotheses., Results: Increased method and decision autonomy, job crafting behaviors, error management orientation, work engagement, and job satisfaction were found in self-managing organizations. Additionally, a surplus of perceived autonomy compared to the ideal autonomy was associated with lower work engagement and job satisfaction compared to a fit between ideal and perceived autonomy. However, job crafting did not relate to a better fit between ideal and perceived autonomy. Decision autonomy predicted higher crafting of challenging demands and structural resources for employees with low error strain. Depending on the autonomy type, learning from errors enhanced or reduced the relationship between perceived autonomy and job crafting., Discussion: This study showed the importance of addressing the higher level of individual autonomy in self-managing organizations and offered starting points for interventions to support employees with handling high autonomy. Reducing error strain but increasing error learning and risking errors could help increase job crafting and work engagement, particularly in self-managing organizations., Competing Interests: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Doblinger.)

The integration of artificial intelligence technologies, such as large language models (LLMs), in health care holds potential for improved efficiency and decision support. However, ethical concerns must be addressed before widespread adoption. This article focuses on the ethical principles surrounding the use of Generative Pretrained Transformer-4 and its conversational model, ChatGPT, in healthcare settings. One concern is potential inaccuracies in generated content. LLMs can produce believable yet incorrect information, risking errors in medical records. Opacity of training data exacerbates this, hindering accuracy assessment. To mitigate, LLMs should train on precise, validated medical data sets. Model bias is another critical concern because LLMs may perpetuate biases from their training, leading to medically inaccurate and discriminatory responses. Sampling, programming, and compliance biases contribute necessitating careful consideration to avoid perpetuating harmful stereotypes. Privacy is paramount in health care, using public LLMs raises risks. Strict data-sharing agreements and Health Insurance Portability and Accountability Act (HIPAA)-compliant training protocols are necessary to protect patient privacy. Although artificial intelligence technologies offer promising opportunities in health care, careful consideration of ethical principles is crucial. Addressing concerns of inaccuracy, bias, and privacy will ensure responsible and patient-centered implementation, benefiting both healthcare professionals and patients., (Copyright © 2023 by the American Academy of Orthopaedic Surgeons.)

The Heart and Flower task is used worldwide to measure age-dependent and individual differences in executive functions and/or cognitive control. The task reliably maps age and individual differences and these have consistently been found to be predictive for different aspects of school readiness and academic achievement. The idea has been put forward that there is a developmental shift in how children approach such a task. While 6-year-olds' tend to adapt their task strategy ad hoc and reactively, older children increasingly engage in proactive cognitive control. Proactive cognitive control entails finding the right response speed without risking errors, always dependent on the cognitive conflict. The main goal of the present contribution was to examine children's adjustments of response speed as a function of age and cognitive conflict by addressing RTs surrounding errors (i.e., errors and post-error trials). Data from a large sample with three age groups was used ( N = 106 6-year-olds' with a mean age of 6 years; 3 months; N = 108 7-year-olds' with a mean age of 7 years; 4 months; N = 78 8-year-olds' with a mean age of 8 years; 1 month). Response speed adjustments and the development thereof were targeted both across the Flower and Mixed block, respectively, and within these blocks focusing on errors and post-error slowing. Results revealed evidence for a developmental shift toward more efficient proactive cognitive control between 6 and 8 years of age, with the older but not the younger children strategically slowing down in the Mixed block and smoother post-error slowing. At the same time, we found that even the youngest age group has emerging proactive cognitive control skills at their disposal when addressing post-error slowing in the Flower block. The present study thus tracks the early roots of later efficient executive functions and cognitive control, contributes to a better understanding of how developmental progression in cognitive control is achieved, and highlights new avenues for research in this domain., Competing Interests: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Roebers.)

The time evolution of the density operator of an AB spin system during a double-spin-echo pulse sequence is evaluated analytically by a computer-algebra system. The computer-algebra system allows one to generate the extensive formulas describing the density operator and yields an expression for the integral of the spectral signals. The simulation of spectra for arbitrary sequence timings can be easily performed by this new tool without risking errors that might occur in conventional calculations. The computer-algebra method can be extended straightforward to other pulse angles and types of sequences. The double-spin-echo pulse sequence is used in the point-resolved spectroscopy (PRESS) method which is often applied for volume selective examinations in vivo. For verification of the results generated by the computer-algebra system, 1H spectra from a half-liter spherical sample with an aqueous solution that was 0.1 M in sodium citrate and 0.1 M in sodium acetate were recorded after 90 degrees-180 degrees-180 degrees double-spin-echo pulse sequences on a 1.5-T whole-body unit. The measured behavior of the citrate AB spin system corresponds very well with the theoretical predictions. Thus, the theory provides the basis for optimization of sequence timings for double-spin-echo measurements with high signal gain from AB systems as, for example, citrate. In addition, the theoretically predicted signal modulations could be fitted to the experimental data, providing the transverse relaxation time of the AB-coupled protons.