Evidence map›Paper›PMID 36988832›Full record

ArticleAdministration and policy in mental health2023

Predicting the Behavioral Health Needs of Asian Americans in Public Mental Health Treatment: A Classification Tree Approach.

Saahoon Hong, Betty Walton, Hea-Won Kim, Taeho Greg Rhee

Abstract read
In one paragraph

Article in Administration and policy in mental health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

4 authors.

Saahoon HongIndiana University School of Social Work, Indianapolis, IN, USA. saahong@iu.edu.ORCID 0000-0002-3187-7062
Betty WaltonIndiana University School of Social Work, Indianapolis, IN, USA.
Hea-Won KimIndiana University School of Social Work, Indianapolis, IN, USA.
Taeho Greg RheeDepartment of Public Health Sciences, School of Medicine, University of Connecticut, Farmington, CT, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Given the fact that experiencing pandemic-related hardship and racial discrimination worsen Asian Americans' mental health, this study aimed to identify unique characteristics of behavioral health needs among Asian Americans (N = 544) compared to White Americans (N = 78,704) and Black Americans (N = 11,252) who received publicly funded behavioral health services in Indiana before and during the COVID-19 pandemic. We used 2019-2020 Adults Needs and Strengths Assessment (ANSA) data for adults eligible for Medicaid or funding from the state behavioral health agency. Chi-squared automatic interaction detection (CHAID) was used to detect race-specific differences among demographic variables, the pandemic status, and ANSA items. Results indicated that, regardless of age, gender, or pandemic status, Asian Americans who received behavioral health services, struggled more with cultural-related factors compared to White and Black individuals. Within this context, intersections among behavioral/emotional needs (psychosis), life functioning needs (involvement in recovery, residential stability, decision making, medical/physical health), and strengths (job history, interpersonal, and spiritual) further differentiated the mental health functioning of Asian from White and Black Americans. Classification tree algorithms offer a promising approach to detecting complex behavioral health challenges and strengths of populations based on race, ethnicity, or other characteristics.

Indexed as

COVID-19Mental HealthAdultAsianEthnicityHumansPandemicsUnited StatesAsian AmericansBehavioral health needsClassification tree modelCulture-related factorsStrengths

Identifiers

PMID36988832
PMCPMC10052246

What OpenQuestion holds

Textmetadata
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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.