Evidence map›Paper›PMID 35707394›Full record

ArticleAEM education and training2022

A lasting impact? Exploring the immediate and longitudinal impact of an emergency department service learning help desk program.

Adam Scott Cohen, Sophia Hu, Margot Bellon, Nancy Ewen Wang, Stefanie S Sebok-Syer

Abstract read
In one paragraph

Article in AEM education and training, 2022. 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. 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

5 authors.

Adam Scott CohenStanford University Palo Alto California USA.ORCID https://orcid.org/0000-0001-6821-322X
Sophia HuUniversity of Pennsylvania Philadelphia Pennsylvania USA.
Margot BellonStanford University Palo Alto California USA.
Nancy Ewen WangStanford University Palo Alto California USA.
Stefanie S Sebok-SyerStanford University Palo Alto California USA.ORCID https://orcid.org/0000-0002-3572-5971

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The emergency department (ED) help desk is an undergraduate-run service learning program that screens ED patients for social needs, connects them to community resources, and follows-up to promote connections with resources. Students accepted to the program participate in a didactic course on the fundamentals of social emergency medicine as well as available community resources. Students also receive training around interviewing patients and use of screening software. Students commit to at least three quarters of service, during which they attend weekly team meetings. Methods: This qualitative study explores the impact of this service learning experience for students. Current and former students were identified by the director of the program. Purposive and snowball sampling was used to select a sample of participants that participated in a semistructured interview. Our codebook was developed inductively using thematic analysis. Themes were presented and discussed with the entire research team for further analysis and refinement. Data collection and analysis used a constant comparative approach, and data collection ceased when saturation was achieved. Results: Study participants consisted of current and former ED help desk student volunteers ( Conclusions: Our ED help desk service learning program offers a unique experience for students to learn about patients' social needs, participate in meaningfully interactions with patients, and empower themselves and patients to work together as coproducers of patients' care.

Identifiers

PMID35707394
PMCPMC9178399

What OpenQuestion holds

Textmetadata
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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.