Evidence map›Paper›PMID 24093349›Full record

Trial reportJournal of health communication2013

Visual causal models enhance clinical explanations of treatments for generalized anxiety disorder.

Nancy S Kim, Danielle Khalife, Kelly A Judge, Daniel J Paulus, Jake T Jordan, Jennelle E Yopchick

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of health communication, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. The progress and promise of health literacy research.Journal of health communication · 2013
    Article
  3. 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

6 authors.

Nancy S Kima Department of Psychology , Northeastern University , Boston , Massachusetts , USA.
Danielle Khalife
Kelly A Judge
Daniel J Paulus
Jake T Jordan
Jennelle E Yopchick

Funding

Visual Causal Models in Clinical ExplanationR03MH081291 · NIMH · NORTHEASTERN UNIVERSITY · PI KIM, NANCY S. · 2009 to 2010
$156k
NIMH NIH HHS MH081291
6 · The paper itself

Abstract

A daily challenge in clinical practice is to adequately explain disorders and treatments to patients of varying levels of literacy in a time-limited situation. Drawing jointly upon research on causal reasoning and multimodal theory, the authors asked whether adding visual causal models to clinical explanations promotes patient learning. Participants were 86 people currently or formerly diagnosed with a mood disorder and 104 lay people in Boston, Massachusetts, USA, who were randomly assigned to receive either a visual causal model (dual-mode) presentation or auditory-only presentation of an explanation about generalized anxiety disorder and its treatment. Participants' knowledge was tested before, immediately after, and 4 weeks after the presentation. Patients and lay people learned significantly more from visual causal model presentations than from auditory-only presentations, and visual causal models were perceived to be helpful. Participants retained some information 4 weeks after the presentation, although the advantage of visual causal models did not persist in the long term. In conclusion, dual-mode presentations featuring visual causal models yield significant relative gains in patient comprehension immediately after the clinical session, at a time when the authors suggest that patients may be most willing to begin the recommended treatment plan.

Indexed as

Audiovisual AidsHealth LiteracyModels, EducationalAdultAnxiety DisordersBostonFemaleFollow-Up StudiesHealth Knowledge, Attitudes, PracticeHumansMaleMiddle AgedPatient Education as Topic

Identifiers

PMID24093349
PMCPMC3814909

What OpenQuestion holds

Textmetadata
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.