Evidence map›Paper›PMID 38790107›Full record

ArticleEvaluation & the health professions2024

Continuous Quality Improvement in the Substance Abuse and Mental Health Services Administration's Technology Transfer Center Network: A Process Evaluation.

Jon Agley, Ruth Gassman, Kaitlyn Reho, Jeffrey Roberts, Susan K R Heil, Graciela Castillo, Lilian Golzarri-Arroyo

Abstract read
In one paragraph

Article in Evaluation & the health professions, 2024. 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. Bed-based care as a response to the overdose crisis: an approach to informing and evaluating proposed expansion.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2025
    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

7 authors.

Jon AgleyIndiana University Bloomington, USA.ORCID 0000-0003-2345-8850
Ruth GassmanIndiana University Bloomington, USA.
Kaitlyn RehoIndiana University Bloomington, USA.
Jeffrey RobertsIndiana University Bloomington, USA.
Susan K R HeilAmerican Institutes for Research (AIR), USA.
Graciela CastilloAmerican Institutes for Research (AIR), USA.
Lilian Golzarri-ArroyoIndiana University Bloomington, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In healthcare and related fields, there is often a gap between research and practice. Scholars have developed frameworks to support dissemination and implementation of best practices, such as the Interactive Systems Framework for Dissemination and Implementation, which shows how scientific innovations are conveyed to practitioners through tools, training, and technical assistance (TA). Underpinning those aspects of the model are evaluation and continuous quality improvement (CQI). However, a recent meta-analysis suggests that the approaches to and outcomes from CQI in healthcare vary considerably, and that more evaluative work is needed. Therefore, this paper describes an assessment of CQI processes within the Substance Abuse and Mental Health Services Administration's (SAMHSA) Technology Transfer Center (TTC) Network, a large TA/TTC system in the United States comprised of 39 distinct centers. We conducted key informant interviews (

Indexed as

Quality ImprovementTechnology TransferUnited States Substance Abuse and Mental Health Services AdministrationHumansMental Health ServicesSubstance-Related DisordersTotal Quality ManagementUnited Statescontinuous quality improvementevaluationinteractive systems frameworkSAMHSAtechnical assistanceTTC

Identifiers

PMID38790107
PMCPMC11157976

What OpenQuestion holds

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LicenceCC BY-NC
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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.