Evidence map›Paper›PMID 36462251›Full record

ArticleJournal of psychiatric research2023

Digitally filling the access gap in mental health care: An investigation of the association between rurality and online engagement with validated self-report screens across the United States.

Elad Yom-Tov, Damien Lekkas, Michael V Heinz, Theresa Nguyen, Paul J Barr, Nicholas C Jacobson

Abstract read
In one paragraph

Article in Journal of psychiatric research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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.

Elad Yom-TovMicrosoft Research Israel, Herzeliya, Israel; Faculty of Industrial Engineering and Management, Technion - Israel Institute of Technology, Haifa, Israel. Electronic address: eladyt@microsoft.com.
Damien LekkasCenter for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, NH, United States.
Michael V HeinzCenter for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Department of Psychiatry, Geisel School of Medicine, Dartmouth College, Hanover, NH, United States.
Theresa NguyenMental Health America, Alexandria, VA, United States.
Paul J BarrCenter for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; The Dartmouth Institute of Health Policy & Clinical Practice, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States.
Nicholas C JacobsonCenter for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States; Quantitative Biomedical Sciences Program, Dartmouth College, Hanover, NH, United States; Department of Psychiatry, Geisel School of Medicine, Dartmouth College, Hanover, NH, United States; Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States.

Funding

Personalized Deep Learning Models of Rapid Changes in Major Depressive Disorder Symptoms using Passive Sensor Data from Smartphones and Wearable DevicesR01MH123482 · NIMH · DARTMOUTH COLLEGE · PI JACOBSON, NICHOLAS CHARLES · 2020 to 2024
$2.6M
NIMH NIH HHS R01 MH123482
6 · The paper itself

Abstract

Mental health disorders are highly prevalent, yet few persons receive access to treatment; this is compounded in rural areas where mental health services are limited. The proliferation of online mental health screening tools are considered a key strategy to increase identification, diagnosis, and treatment of mental illness. However, research on real-world effectiveness, especially in hard to reach rural communities, is limited. Accordingly, the current work seeks to test the hypothesis that online screening use is greater in rural communities with limited mental health resources. The study utilized a national, online, population-based cohort consisting of Microsoft Bing search engine users across 18 months in the United States (representing approximately one-third of all internet searches), in conjunction with user-matched data of completed online mental health screens for anxiety, bipolar, depression, and psychosis (N = 4354) through Mental Health America, a leading non-profit mental health organization in the United States. Rank regression modeling was leveraged to characterize U.S. county-level screen completion rates as a function of rurality, health-care availability, and sociodemographic variables. County-level rurality and mental health care availability alone explained 42% of the variance in MHA screen completion rate (R

Indexed as

Mental HealthRural PopulationHealth Services AccessibilityHumansSelf ReportSurveys and QuestionnairesUnited StatesDigital healthHealth-care accessHealth-care equityInternet search behaviorMental healthOnline screen

Identifiers

PMID36462251
PMCPMC9898139

What OpenQuestion holds

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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.