Evidence map›Paper›PMID 38406240›Full record

ArticlePRiMER (Leawood, Kan.)2024

Evaluating Barriers to Opioid Use Disorder Treatment From Patients' Perspectives.

Cecilia M T Nguyen, Grace Kubiak, Neil Dixit, Staci A Young, John R Hayes

Abstract read
In one paragraph

Article in PRiMER (Leawood, Kan.), 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. Review
  2. Article
  3. Review
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  5. 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

5 authors.

Cecilia M T Nguyen *Medical College of Wisconsin, Milwaukee, WI.
Grace Kubiak *Medical College of Wisconsin, Milwaukee, WI.
Neil Dixit *Medical College of Wisconsin, Milwaukee, WI.
Staci A YoungDepartment of Family & Community Medicine, Medical College of Wisconsin, Milwuakee, WI.
John R HayesMedical College of Wisconsin, Department of Community and Family Medicine, Milwaukee, WI.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Utilizing medications to treat opioid use disorder (MOUD) is both highly effective and unfortunately underutilized in the US health care system. Stigma surrounding substance use disorders, insufficient provider knowledge about substance use disorders and MOUD, and historical lack of physicians with X-waivers to prescribe buprenorphine contribute to this underutilization. Our study aimed to elucidate barriers to accessing MOUD in Milwaukee, Wisconsin. Methods: We conducted semistructured interviews with patients receiving MOUD at a family medicine residency program in Milwaukee, Wisconsin. Interviews were audio-recorded, transcribed verbatim, and analyzed using the qualitative analysis Framework Method. Researchers in our team reviewed transcripts, coding for specific topics of discussion. Coded transcript data were then sorted into a matrix to identify common themes. Results: Interviews with 30 participants showed that motivations to seek treatment appeared self-driven and/or for loved ones. Eighteen patients noted concerns with treatment including treatment denial and efficacy of treatment. Housing instability, experiences with incarceration, insurance, and transportation were common structural barriers to treatment. Conclusions: Primary drivers to seek treatment were patients themselves and/or loved ones. Barriers to care include lack of effective transportation, previous experience with the carceral system, and relative scarcity of clinicians offering MOUD. Future studies may further explore effects of structural inadequacies and biases on MOUD access and quality.

Identifiers

PMID38406240
PMCPMC10887383

What OpenQuestion holds

Textmetadata
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Registered trials

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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.