Evidence map›Paper›PMID 38661862›Full record

ArticleJournal of health care for the poor and underserved2024

Experiences of Stigma and Discrimination Compounded by Intersecting Identities among Individuals Receiving Medication for Opioid Use Disorder.

Uzoji Nwanaji-Enwerem, Nancy S Redeker, Meghan O'Connell, Declan Barry, Theddeus Iheanacho, Tish M Knobf, Dustin Scheinost, Katie Wang, Klar Yaggi, Lois S Sadler

Abstract read
In one paragraph

Article in Journal of health care for the poor and underserved, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
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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

10 authors.

Uzoji Nwanaji-Enwerem
Nancy S Redeker
Meghan O'Connell
Declan Barry
Theddeus Iheanacho
Tish M Knobf
Dustin Scheinost
Katie Wang
Klar Yaggi
Lois S Sadler

Funding

The Collaboration Linking Opioid Use Disorder and Sleep ("CLOUDS") StudyU01HL150596 · NHLBI · YALE UNIVERSITY · PI BARRY, DECLAN T., REDEKER, NANCY S · 2019 to 2024
$5.1M
A Mixed Methods Exploration of Stigma, Discrimination, and Sleep Among Those on Medication for Opioid Use DisorderF31DA054762 · NIDA · YALE UNIVERSITY · PI NWANAJI-ENWEREM, UZOJI · 2022 to 2023
$70k
NHLBI NIH HHS U01 HL150596NIDA NIH HHS F31 DA054762
6 · The paper itself

Abstract

Stigma and discrimination create barriers to care among people receiving medication for opioid use disorder (MOUD). We report qualitative findings from a mixed methods study guided by three aims: to explore (1) intersecting identities of people receiving MOUD (2) how individuals experience stigma and discrimination and (3) helpful resources in addressing cumulative experiences of multiple forms of disadvantage. We conducted interviews with 25 individuals in three treatment centers in the Northeast United States and identified six themes: (1) Living with multiple socially marginalized identities and addiction; (2) Loss; (3) "It's everywhere": Discrimination and stigma; (4) A "damaged" identity, (5) Positive responses to negative experiences: Facing reality and becoming accountable, and (6) Experiencing treatment and identifying supportive interventions. Findings highlight the complexity of intersecting, marginalized social positions. Future work should look beyond one-size-fits-all approaches to care and recognize individual vulnerabilities and strengths for improving outcomes among those experiencing OUD.

Indexed as

Opioid-Related DisordersSocial StigmaAdultFemaleHumansInterviews as TopicMaleMiddle AgedNew EnglandOpiate Substitution TreatmentQualitative ResearchSocial Discrimination

Identifiers

PMID38661862
PMCPMC12908579

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.