Evidence map›Paper›PMID 32818196›Full record

ArticlePRiMER (Leawood, Kan.)2018

Text Messaging as a Teaching Tool in a Family Medicine Clerkship Rotation.

Scott Bragg, Kristen Hood Watson, Donna Kern, Andrea Wessell, Alexander W Chessman

Abstract read
In one paragraph

Article in PRiMER (Leawood, Kan.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Scott BraggDepartment of Family Medicine, Medical University of South Carolina, and Clinical Pharmacy and Outcomes Sciences, Medical University of South Carolina College of Pharmacy, Charleston, SC.
Kristen Hood WatsonDepartment of Family Medicine, Medical University of South Carolina, Charleston, SC.
Donna KernDepartment of Family Medicine, Medical University of South Carolina, Charleston, SC.
Andrea WessellDepartment of Family Medicine, Medical University of South Carolina, Charleston, SC.
Alexander W ChessmanDepartment of Family Medicine, Medical University of South Carolina, Charleston, SC.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionToday's learners use multiple forms of social communication, such as text messaging, that offer a promising teaching tool for medical education. The purpose of this study was to evaluate a diabetes care curriculum delivered through text messages for third-year medical students on a rural family medicine clerkship.

methodsA pilot study of 119 participants were compared in a parallel group randomized controlled trial evaluating medical student learning and satisfaction with text messages throughout rotation compared to an email with the same content in their first week of rotation. Participants completed a 10-question multiple-choice test and six survey questions upon completing the rotation. The primary outcome was a difference between test scores among the two groups, and student satisfaction with the educational intervention was a secondary outcome.

resultsA total of 85 participants successfully completed the study protocol (34 text messages and 51 email) and were included in a per protocol analysis. The average number of correct responses per test was 3.32 (SD 1.29) in the texting group and 3.69 (SD 1.53) in the email group (

conclusionsParticipant knowledge on a challenging posttest was not improved with text messages compared to an email in this pilot study. Satisfaction with text messages was primarily positive. Further study is needed to determine the effectiveness of this content delivery method.

Identifiers

PMID32818196
PMCPMC7426130

What OpenQuestion holds

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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.