A mixed-methods study of emotional support for families of organ donors in Hunan Province, China.

Background: Family consent is a prerequisite for the organ donation of the deceased in China. However, a large number of donors are individuals who died due to accidental injuries or unanticipated diseases, which means that most of the families of such donors have just experienced the sudden death of their loved one and have to make a donation decision in a short time. This decision may cause psychological stress and some psychological damage to the minds of relatives of the donors. In addition, cultural sensitivity also has largely caused the relatives of donors inner conflicts and contradictions. And sometimes organ donation may still be stigmatized. However, have they received any emotional support and what is their emotional support needs are some questions that need to be answered. Therefore, this study aims to investigate the emotional support, influencing factors, and needs of the family members of organ donors in Hunan Province, China. Materials and methods: This is mixed-methods research that combines quantitative and qualitative research methods. A cross-sectional survey was conducted among 102 donor families using a questionnaire to investigate their emotional support status. To further understand their emotional support needs, 12 donor families participated in the semi-structured interview. Results: The results confirmed that: (1) A total of 67.7% of the 102 respondents received emotional support or psychological comfort. Thus, only a small number of respondents (31.4%) felt respected by the public. (2) Emotional support came mainly from immediate family members (73.91%), and official organizations such as the Red Cross (43.48%). (3) Marital status, health status, occupation, and coping style can affect the emotional support of the donor families (p < 0.05). (4) Interview showed that the families of donors need emotional support and psychological aid from psychological professionals mostly. And they also wish to receive the understanding and respect of the public. Conclusion: Most families of organ donors received emotional support from family, Red Cross, and friends, but only a minority of families of donors reported receiving respect from the public after the donation. And families of donor showed a strong need for emotional support and professional psychological aid from institutions.

Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings.

Machine learning for materials discovery has largely focused on predicting an individual scalar rather than multiple related properties, where spectral properties are an important example. Fundamental spectral properties include the phonon density of states (phDOS) and the electronic density of states (eDOS), which individually or collectively are the origins of a breadth of materials observables and functions. Building upon the success of graph attention networks for encoding crystalline materials, we introduce a probabilistic embedding generator specifically tailored to the prediction of spectral properties. Coupled with supervised contrastive learning, our materials-to-spectrum (Mat2Spec) model outperforms state-of-the-art methods for predicting ab initio phDOS and eDOS for crystalline materials. We demonstrate Mat2Spec's ability to identify eDOS gaps below the Fermi energy, validating predictions with ab initio calculations and thereby discovering candidate thermoelectrics and transparent conductors. Mat2Spec is an exemplar framework for predicting spectral properties of materials via strategically incorporated machine learning techniques.