Evidence map›Paper›PMID 41783601›Full record

ArticleSocial work in mental health2025

Applying machine learning to understand factors predicting pharmacotherapy for mental health support among adults with intellectual and developmental disabilities.

Matthew Bogenschutz, Michael Broda, Parthenia Dinora, Seb Prohn, Sarah Lineberry, Angela West

Abstract read
In one paragraph

Article in Social work in mental health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Matthew BogenschutzSchool of Social Work, Virginia Commonwealth University, Richmond, USA.
Michael BrodaSchool of Education, Virginia Commonwealth University, Richmond, USA.
Parthenia DinoraPartnership for People with Disabilities, Virginia Commonwealth University, Richmond, USA.
Seb ProhnPartnership for People with Disabilities, Virginia Commonwealth University, Richmond, USA.
Sarah LineberryPartnership for People with Disabilities, Virginia Commonwealth University, Richmond, USA.
Angela WestPartnership for People with Disabilities, Virginia Commonwealth University, Richmond, USA.

Funding

ACL HHS 90IFRE0047
6 · The paper itself

Abstract

Americans with intellectual and developmental disabilities (IDD) experience health disparities, including in their mental health. This often leads to disproportionate use of psychotropic medications, sometimes leading to serious side effects. We used machine learning to analyze an integrated dataset (years 2018-2022) from one U.S. state with 2907 observations and 850 variables to determine what factors were most predictive of pharmacotherapy use to support mental health needs among people with IDD. Our algorithm performed strongly, with the presence of mood, anxiety, and psychotic disorders, documented behavioral support needs, and overall support needs all contributing strongly to the algorithm's accuracy. Implications for social workers and other mental health professionals are discussed.

Indexed as

Intellectual and developmental disabilitiesmachine learningmental healthpharmacotherapysocial work

Identifiers

PMID41783601
PMCPMC12955809

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.