Experiences of Siblings of Children With Neurodevelopmental Disorders: Comparing Qualitative Analysis and Machine Learning to Study Narratives

"Broken fragments or a breathtaking mosaic": A mixed methods study of self-reported attributes and aspirations of siblings of individuals with and without neurodevelopmental conditions

Siblings of individuals with neurodevelopmental conditions (NDCs) experience distinct challenges and have unique strengths compared to siblings of individuals without NDCs. The present study examined attributes and aspirations of siblings of individuals with and without neurodevelopmental conditions, and analyzed the association between qualitative responses and quantitative measures of growth mindset, positive and negative valence, and mental health diagnoses. A novel mixed methods thematic analysis was employed to explore the experiences of 166 siblings (75 NDC and 91 controls, aged 14-26, 66.27% female) completing an online survey as part of a larger study on sibling mental health. The overarching theme described The Process of Self-Actualization and Integration, reflecting the journey siblings undergo in seeking to understand themselves and others amidst psychological challenges. It encompassed three subthemes: Personal Growth and Identity Formation; Connection and Belonginess; and Societal Perspective and Global Consciousness. Qualitative responses were analyzed within a Research Domain Criteria (RDoC) framework, and associations between phenomenology and mental health diagnoses examined. NDC siblings had higher negative valence and lower positive valence embedded in their responses, and quantitatively lower self-reported growth mindset (i.e., beliefs about the capacity for personal growth), compared to control siblings, which correlated with self-reported mental health diagnoses. Findings suggest clinical practice may focus on optimizing self-identified strengths and offer opportunities for self-actualization of hopes and ambitions, while providing support for families to attenuate bioecological factors impacting mental health.

Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions

BACKGROUND

The field of implementation science was developed to address the significant time delay between establishing an evidence-based practice and its widespread use. Although implementation science has contributed much toward bridging this gap, the evidence-to-practice chasm remains a challenge. There are some key aspects of implementation science in which advances are needed, including speed and assessing causality and mechanisms. The increasing availability of artificial intelligence applications offers opportunities to help address specific issues faced by the field of implementation science and expand its methods.

MAIN TEXT

This paper discusses the many ways artificial intelligence can address key challenges in applying implementation science methods while also considering potential pitfalls to the use of artificial intelligence. We answer the questions of "why" the field of implementation science should consider artificial intelligence, for "what" (the purpose and methods), and the "what" (consequences and challenges). We describe specific ways artificial intelligence can address implementation science challenges related to (1) speed, (2) sustainability, (3) equity, (4) generalizability, (5) assessing context and context-outcome relationships, and (6) assessing causality and mechanisms. Examples are provided from global health systems, public health, and precision health that illustrate both potential advantages and hazards of integrating artificial intelligence applications into implementation science methods. We conclude by providing recommendations and resources for implementation researchers and practitioners to leverage artificial intelligence in their work responsibly.

CONCLUSIONS

Artificial intelligence holds promise to advance implementation science methods ("why") and accelerate its goals of closing the evidence-to-practice gap ("purpose"). However, evaluation of artificial intelligence's potential unintended consequences must be considered and proactively monitored. Given the technical nature of artificial intelligence applications as well as their potential impact on the field, transdisciplinary collaboration is needed and may suggest the need for a subset of implementation scientists cross-trained in both fields to ensure artificial intelligence is used optimally and ethically.

Risk and resilience factors impacting the mental health and wellbeing of siblings of individuals with neurodevelopmental conditions: A mixed methods systematic review

OBJECTIVE

This pre-registered systematic review synthesised and evaluated the existing literature on self-reported mental health and wellbeing of siblings of individuals with neurodevelopmental conditions (NDCs).

METHODS

From 2437 identified studies published 2000-2022, 81 studies were included: 14 population- or cohort-based, 39 quantitative, 7 mixed method, and 21 qualitative outcome studies.

RESULTS

Seven sibling mental health (any psychiatric disorder, anxiety, depression, bipolar disorder, schizophrenia, internalising and externalising difficulties) and five wellbeing indicators were identified (quality of life, emotional adjustment, social wellbeing, somatic/physical wellbeing, and resilience/growth). Overall, siblings had increased risk of any psychiatric disorder, but they also reported experiences of growth and resilience, primarily in qualitative studies. 41 risk factors and 24 resilience factors associated with these outcomes were identified; the most frequently cited risk factor was symptom severity of the NDC sibling, while the most common resilience factor was adaptive/active coping at the individual sibling level. Studies showed high methodological heterogeneity and 90 different self-report measures were used.

CONCLUSIONS

Sibling mental health indictors are heterogeneous and cumulative risk factors may result in poorer wellbeing. There is a need for consistent reporting of family and sibling characteristics, a strengths-based approach to assessment, and identification of protective and resilience-promoting factors.