Association between Quitline Facility Type and Subsequent Client Contact.

Stephen S Michael, Ryan G N Seltzer, Mignonne C Guy

Abstract read

In one paragraph

Article in Health behavior and policy review, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Stephen S MichaelStephen S. Michael, Zuckerman College of Public Health, University of Arizona, Tucson, AZ. Ryan G.N. Seltzer, Zuckerman College of Public Health, University of Arizona, Tucson, AZ. Mignonne C. Guy, Department of African American Studies, College of Humanities and Sciences, Virginia Commonwealth University, Richmond, VA.

Ryan G N SeltzerStephen S. Michael, Zuckerman College of Public Health, University of Arizona, Tucson, AZ. Ryan G.N. Seltzer, Zuckerman College of Public Health, University of Arizona, Tucson, AZ. Mignonne C. Guy, Department of African American Studies, College of Humanities and Sciences, Virginia Commonwealth University, Richmond, VA.

Mignonne C GuyStephen S. Michael, Zuckerman College of Public Health, University of Arizona, Tucson, AZ. Ryan G.N. Seltzer, Zuckerman College of Public Health, University of Arizona, Tucson, AZ. Mignonne C. Guy, Department of African American Studies, College of Humanities and Sciences, Virginia Commonwealth University, Richmond, VA.

Funding

Training and Education Core p270-287P50DA036105 · NIDA · VIRGINIA COMMONWEALTH UNIVERSITY · PI EISSENBERG, THOMAS EVAN · 2013 to 2017

$19.2M

NIDA NIH HHS P50 DA036105

6 · The paper itself

Abstract

Objectives: We conducted secondary analysis on the Arizona Smokers' Helpline data from 2009 to 2011 to assess the relationship between type of referring facility and number of referred patients reached, enrolled, and quit 7 months post-enrollment. Methods: We used logistic regression to compare rates for hospitals, community health centers, and primary service providers. Results: Staff members were more likely to reach patients referred by community health centers and to enroll patients referred by primary service providers. At 7 months post-enrollment, there were no differences in the 30-day point prevalence quit rates. Conclusions: Patients referred to quitlines engage at different rates depending on facility type. Training of healthcare professionals may need to differ by facility type.

Indexed as

counselinghealthcare disparitiespublic health practicesmokingtobacco

Identifiers

PMID42376072

DOI10.14485/hbpr.2.3.7

PMCPMC13313612

What OpenQuestion holds

Textmetadata

LicenceTDM

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.