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

Artificial Intelligence and Qualitative Analysis of Emergency Department Telemental Health Care Implementation Survey

Background: Implementation of telemental health care in emergency departments (EDs) in the United States (U.S.) has been increasing. Artificial intelligence (AI) can augment traditional qualitative research methods; little is known about its efficiency and accuracy. This study sought to understand ED directors' qualitative recommendations for improving telemental health care implementation and to understand how AI could facilitate analysis of qualitative survey responses. Methods: Directors at a nationally representative sample of 279 U.S. EDs that used telemental health care completed an open-ended survey question about improving telemental health care implementation between June 2022 and October 2023. Two groups of researchers completed independent qualitative coding of responses: one group used traditional qualitative methods, and one group used AI (ChatGPT 4.0) to facilitate analysis. Both groups independently developed a codebook, came to consensus on a combined codebook, and each group independently used it to code the survey responses. The two groups identified themes in ED directors' recommendations and compared codebooks and code application across traditional and AI approaches. Results: Themes included (1) recommendations for improving telemental health care directly and (2) recommendations for improving mental health care systems broadly to make telehealth more effective. ED directors' most common recommendation was enabling faster and more streamlined access to telemental health care. AI augmented human coding by identifying two valid codes not initially identified by human analysts. In codebook application, 75% of responses were coded consistently across AI and human coders. Conclusions and Relevance: For US EDs using telemental health care, there is a need to improve timeliness and efficiency of access to telemental health care.

Perceived Impact of COVID-19 in an Underserved Community: A Natural Language Processing Approach

AIM

To utilise natural language processing (NLP) to analyse interviews about the impact of COVID-19 in underserved communities and to compare it to traditional thematic analysis in a small subset of interviews.

DESIGN

NLP and thematic analysis were used together to comprehensively examine the interview data.

METHODS

Fifty transcribed interviews with purposively sampled adults living in underserved communities in the United States, conducted from June 2021 to May 2022, were analysed to explore the impact of the COVID-19 pandemic on social activities, mental and emotional stress and physical and spiritual well-being. NLP includes several stages: data extraction, preprocessing, processing using word embeddings and topic modelling and visualisation. This was compared to thematic analysis in a random sample of 10 interviews.

RESULTS

Six themes emerged from thematic analysis: The New Normal, Juxtaposition of Emotions, Ripple Effects on Health, Brutal yet Elusive Reality, Evolving Connections and Journey of Spirituality and Self-Realisation. With NLP, four clusters of similar context words for each approach were analysed visually and numerically. The frequency-based word embedding approach was most interpretable and well aligned with the thematic analysis.

CONCLUSION

The NLP results complemented the thematic analysis and offered new insights regarding the passage of time, the interconnectedness of impacts and the semantic connections among words. This research highlights the interdependence of pandemic impacts, simultaneously positive and negative effects and deeply individual COVID-19 experiences in underserved communities.

IMPLICATIONS

The iterative integration of NLP and thematic analysis was efficient and effective, facilitating the analysis of many transcripts and expanding nursing research methodology.

IMPACT

While thematic analysis provided richer, more detailed themes, NLP captured new elements and combinations of words, making it a promising tool in qualitative analysis.

REPORTING METHOD

Not applicable.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

"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.