Evidence map›Paper›PMID 29937252›Full record

ArticleBehavior therapy2018

Supervision in Community Mental Health: Understanding Intensity of EBT Focus.

Leah Lucid, Rosemary Meza, Michael D Pullmann, Nathaniel Jungbluth, Esther Deblinger, Shannon Dorsey

Abstract read
In one paragraph

Article in Behavior therapy, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 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.

Leah LucidUniversity of Washington. Electronic address: llucid@uw.edu.
Rosemary MezaUniversity of Washington.
Michael D PullmannUniversity of Washington School of Medicine.
Nathaniel JungbluthUniversity of Washington.
Esther DeblingerRowan University School of Osteopathic Medicine.
Shannon DorseyUniversity of Washington.

Funding

Improving Practice in Community-based Settings: A Randomized Trial of SupervisionR01MH095749 · NIMH · UNIVERSITY OF WASHINGTON · PI DORSEY, SHANNON · 2012 to 2016
$2.5M
NIMH NIH HHS R01 MH095749
6 · The paper itself

Abstract

The goal of the present study was to examine clinician, supervisor, and organizational factors that are associated with the intensity of evidence-based treatment (EBT) focus in workplace-based clinical supervision of a specific EBT, Trauma-Focused Cognitive Behavioral Therapy (TF-CBT). Supervisors (n = 56) and clinicians (n = 207) from mental health organizations across Washington State completed online self-report questionnaires. Multilevel modeling (MLM) analyses were used to examine the relative influence of nested clinician and supervisor factors on the intensity of EBT focus in supervision. We found that 33% of the variance in clinician report of EBT supervision intensity clustered at the supervisor level and implementation climate was the only significant factor associated with EBT supervision intensity. While individual clinician and supervisor factors may play a role in EBT coverage in supervision, our results suggest that an implementation climate that supports EBT may be the most critical factor for improving intensity of EBT coverage. Thus, implementation efforts that address the extent to which EBTs are expected, rewarded, and supported within an organization may be needed to support greater coverage of EBT during workplace-based supervision.

Indexed as

AdultCognitive Behavioral TherapyCommunity Mental Health ServicesCross-Sectional StudiesEvidence-Based MedicineFemaleHumansMaleMental HealthMiddle AgedMotivationSurveys and Questionnairescommunity mental healthevidence-based treatmentimplementation climatesupervision

Identifiers

PMID29937252
PMCPMC6020167

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.