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Using Machine Learning to Identify Most Salient Factors Impacting Disordered Eating Risk and Treatment Among Rural Adolescents

NIMH - National Institute of Mental Health

open
Open

About This Grant

PROJECT SUMMARY Disordered eating is a critical public health issue in the United States, due to the alarmingly high prevalence, and myriad of negative physical and psychosocial consequences. Adolescence is a critical developmental period for both the prevention and treatment of disordered eating. Many physical and social changes that occur in adolescence increase disordered eating risk, and early treatment intervention is imperative for treatment prognosis. However, social and environmental differences lead to high prevalence and worse clinical outcomes for disordered eating among rural adolescents. Rural adolescents face rates of disordered eating that are approximately double compared to nationally representative samples. Alarmingly high rates of disordered eating among rural adolescents are likely because they face unique social and structural influences that both increase the likelihood of developing disordered eating but also leads to lower likelihood of treatment. Factors that have been shown to be associated with increased risk of disordered eating in other populations are elevated in rural adolescents but also may be more iatrogenic in rural communities. For example, food insecurity is more common in rural populations, but harm may be further compounded by lack of access to healthful foods in rural communities. Additionally, rural adolescents may be more likely to delay or never receive care because of reduced treatment access and social stigma around mental health that is particularly pervasive in rural communities. It is also likely that factors such as social connectedness and body functionality appreciation may be uniquely protective for disordered eating in the rural context. However, there has never been a study designed to examine disordered eating risk factors, protective factors, or treatment obstacles specifically within rural adolescent populations. The proposed study would fill a critical gap in identifying the most salient risk factors, protective factors, and treatment obstacles as well as population-identified solutions within the context of rural adolescents. The study findings can be used to develop culturally relevant prevention and treatment interventions to reduce disordered eating rates among rural adolescents. We will use a concurrent triangulation mixed-methods approach including a cross-sectional survey (N=1,000) among rural high school students that will be analyzed using random forest algorithms, a machine learning technique, and semi-structured interviews (N=40) among rural high school students experiencing disordered eating, who both have and have not received treatment, to achieve the following specific aims: AIM 1: Determine the most salient risk/protective factors that predict disordered eating among rural emerging adults. AIM 2: Identify the most salient obstacles to disordered eating treatment that exist in rural communities and identify population- identified solutions to treatment obstacles.

Grant Summary

Using Machine Learning to Identify Most Salient Factors Impacting Disordered Eating Risk and Treatment Among Rural Adolescents is a NIMH - National Institute of Mental Health grant providing up to $559K for university, nonprofit, healthcare org. Applications are due 2029-08-14 (open). Check eligibility and apply with FindGrants.

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Focus Areas

health research

Eligibility

universitynonprofithealthcare org

How to Apply

Funding Range

Up to $559K

Deadline

2029-08-14

Complexity
Medium
  1. 1Confirm your organization is eligible for Using Machine Learning to Identify Most Salient Factors Impacting Disordered Eating Risk and Treatment Among Rural Adolescents from NIMH - National Institute of Mental Health, checking organization type, location, and any population or project requirements.
  2. 2Gather the required documents and information, including your organization details, project plan, and budget figures.
  3. 3Draft your application narrative and budget addressing the funder's priorities and review criteria. FindGrants can draft each section for you to review and edit.
  4. 4Review every section against the requirements checklist, then export a submission-ready application pack and submit it to NIMH - National Institute of Mental Health before the deadline.
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Using Machine Learning to Identify Most Salient Factors Impacting Disordered Eating Risk and Treatment Among Rural Adolescents: Frequently Asked Questions

Who is eligible for the Using Machine Learning to Identify Most Salient Factors Impacting Disordered Eating Risk and Treatment Among Rural Adolescents?

Using Machine Learning to Identify Most Salient Factors Impacting Disordered Eating Risk and Treatment Among Rural Adolescents is offered by NIMH - National Institute of Mental Health and is generally open to university, nonprofit, healthcare org. It is open to organizations nationwide unless the funder specifies otherwise. Review the specific eligibility terms before applying, since funders set their own requirements around organization type, location, and the population or project being served.

How much funding does the Using Machine Learning to Identify Most Salient Factors Impacting Disordered Eating Risk and Treatment Among Rural Adolescents provide?

Using Machine Learning to Identify Most Salient Factors Impacting Disordered Eating Risk and Treatment Among Rural Adolescents provides up to $559K per award from NIMH - National Institute of Mental Health. Actual award sizes depend on the scope of your project, available program funds, and the number of applicants, so build a budget that reflects realistic, allowable costs rather than the maximum figure.

When is the Using Machine Learning to Identify Most Salient Factors Impacting Disordered Eating Risk and Treatment Among Rural Adolescents deadline?

Applications for Using Machine Learning to Identify Most Salient Factors Impacting Disordered Eating Risk and Treatment Among Rural Adolescents are due 2029-08-14 (open). Because deadlines can change, verify the date with the funder, NIMH - National Institute of Mental Health, and give yourself enough time to prepare a complete, competitive application before the close date.

How do you apply for the Using Machine Learning to Identify Most Salient Factors Impacting Disordered Eating Risk and Treatment Among Rural Adolescents?

To apply for Using Machine Learning to Identify Most Salient Factors Impacting Disordered Eating Risk and Treatment Among Rural Adolescents, confirm your eligibility, gather the required documents, and prepare a narrative and budget that address the funder's priorities. FindGrants guides you step by step and can draft each section, then exports a submission-ready application pack for this grant from NIMH - National Institute of Mental Health.