Unmet Needs of Male Caregivers of Children and Youth with Special Health Care Needs.

OBJECTIVES: The caregiving experiences and unique health needs of male caregivers of children and youth with special health care needs (CYSHCN) are not well described. This study seeks to understand potential unmet health needs and attitudes toward supportive resources from the perspective of a sample of male caregivers of CYSHCN. METHODS: This mixed-methods study recruited a convenience sample of 30 men with CYSHCN who receive care for a chronic medical condition from primary care medical homes in Pennsylvania. We conducted semi-structured interviews (SSI), administered quantitative surveys to caregivers, and produced a thematic analysis. The SSI explored the health needs of male caregivers, assessed attitudes toward and preferences regarding supportive resources, and garnered their advice to other caregivers regarding parenting and health care system navigation. RESULTS: Participants' median age is 41 years (IQR: 33, 44), and most (80%) reside full time with their CYSHCN. Most male caregivers deprioritized their own emotional, mental, and physical health needs to support the needs of their families. Many male caregivers articulated interest in seeking emotionally supportive resources (not time-intensive). They advised other male caregivers to remain involved in the medical care and wellbeing of their CYSCHN and to openly seek and receive emotional support despite the daily challenges they face. CONCLUSIONS FOR PRACTICE: Male caregivers of CYSHCN experience intense daily stress and express the need for emotionally supportive resources. Programs designed to facilitate such support may benefit from flexible formats (time and location) and involve male caregivers of CYSHCN as both facilitators and participants.

Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis.

Most chemical experiments are planned by human scientists and therefore are subject to a variety of human cognitive biases1, heuristics2 and social influences3. These anthropogenic chemical reaction data are widely used to train machine-learning models4 that are used to predict organic5 and inorganic6,7 syntheses. However, it is known that societal biases are encoded in datasets and are perpetuated in machine-learning models8. Here we identify as-yet-unacknowledged anthropogenic biases in both the reagent choices and reaction conditions of chemical reaction datasets using a combination of data mining and experiments. We find that the amine choices in the reported crystal structures of hydrothermal synthesis of amine-templated metal oxides9 follow a power-law distribution in which 17% of amine reactants occur in 79% of reported compounds, consistent with distributions in social influence models10-12. An analysis of unpublished historical laboratory notebook records shows similarly biased distributions of reaction condition choices. By performing 548 randomly generated experiments, we demonstrate that the popularity of reactants or the choices of reaction conditions are uncorrelated to the success of the reaction. We show that randomly generated experiments better illustrate the range of parameter choices that are compatible with crystal formation. Machine-learning models that we train on a smaller randomized reaction dataset outperform models trained on larger human-selected reaction datasets, demonstrating the importance of identifying and addressing anthropogenic biases in scientific data.