Evidence map›Paper›PMID 38726224›Full record

ArticleJournal of psychiatry and brain science2024

Closing the Digital Divide in Interventions for Substance Use Disorder.

Jazmin Hampton, Purity Mugambi, Emily Caggiano, Reynalde Eugene, Alycia Valente, Melissa Taylor, Stephanie Carreiro

Abstract read
In one paragraph

Article in Journal of psychiatry and brain science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 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

7 authors.

Jazmin HamptonDivision of Toxicology, Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.
Purity MugambiManning College of Information and Computer Sciences, University of Massachusetts-Amherst, Amherst, MA 01003, USA.
Emily CaggianoDivision of Toxicology, Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.
Reynalde EugeneDivision of Toxicology, Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.
Alycia ValenteDivision of Toxicology, Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.
Melissa TaylorDivision of Toxicology, Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.
Stephanie CarreiroDivision of Toxicology, Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA.

Funding

RAE cHealth: A digital community support tool to promote recovery from substance use disorderR44DA056162 · NIDA · CONTINUEYOU, LLC · PI CARREIRO, STEPHANIE P, REINHARDT, MEGAN ROIS · 2022 to 2024
$2.0M
RAE (Realize, Analyze, Engage)- A Digital Biomarker Based Detection and Intervention System for Stress and Craving During Recovery from Substance Abuse DisordersR44DA046151 · NIDA · CONTINUEYOU, LLC · PI CARREIRO, STEPHANIE P, GILBERTSON, NICOLE · 2019 to 2022
$1.8M
The ANTIDOTE Institute- Advancing New Toxicology Investigators in Drug abuse and Original Translational research EffortsR25DA058490 · NIDA · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI STEPHANIE P CARREIRO, Peter R Chai · 2023 to 2026
$1.2M
NIDA NIH HHS R25 DA058490NIDA NIH HHS R44 DA046151NIDA NIH HHS R44 DA056162
6 · The paper itself

Abstract

Digital health interventions are exploding in today's medical practice and have tremendous potential to support the treatment of substance use disorders (SUD). Developers and healthcare providers alike must be cognizant of the potential for digital interventions to exacerbate existing inequities in SUD treatment, particularly as they relate to Social Determinants of Health (SDoH). To explore this evolving area of study, this manuscript will review the existing concepts of the digital divide and digital inequities, and the role SDoH play as drivers of digital inequities. We will then explore how the data used and modeling strategies can create bias in digital health tools for SUD. Finally, we will discuss potential solutions and future directions to bridge these gaps including smartphone ownership, Wi-Fi access, digital literacy, and mitigation of historical, algorithmic, and measurement bias. Thoughtful design of digital interventions is quintessential to reduce the risk of bias, decrease the digital divide, and create equitable health outcomes for individuals with SUD.

Indexed as

algorithmic biasartificial intelligencedigital dividedigital healthdigital inequitiesmachine learningmHealthsocial determinants of healthsubstance use disorder

Identifiers

PMID38726224
PMCPMC11081399

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.