Evidence map›Paper›PMID 35558728›Full record

ArticleNeurotrauma reports2022

Return to Learn: Preferences of College Educators When Receiving Concussion Medical Notes.

Zachary W Bevilacqua, Donetta Cothran, Devin Rettke, David Koceja, Thomas Nelson-Laird, Keisuke Kawata

Abstract read
In one paragraph

Article in Neurotrauma reports, 2022. 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. 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.

Zachary W BevilacquaDepartment of Exercise Science, Wegmans School of Health and Nutrition, Rochester Institute of Technology, Rochester, New York, USA.
Donetta CothranDepartment of Kinesiology, School of Public Health, School of Education, Indiana University Bloomington, Bloomington, Indiana, USA.
Devin RettkeDepartment of Kinesiology, School of Public Health, School of Education, Indiana University Bloomington, Bloomington, Indiana, USA.
David KocejaDepartment of Kinesiology, School of Public Health, School of Education, Indiana University Bloomington, Bloomington, Indiana, USA.
Thomas Nelson-LairdDepartment of Postsecondary Research, School of Education, Indiana University Bloomington, Bloomington, Indiana, USA.
Keisuke KawataDepartment of Kinesiology, School of Public Health, School of Education, Indiana University Bloomington, Bloomington, Indiana, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this work is to uncover the preferences and perspectives of college educators as they interpret medical documentation outlining medically requested return-to-learn (RTL) instructions. Participants were recruited from five colleges across campus at a large Midwest public university. They each engaged in a private, one-on-one, audio-recorded interview. All recordings were transcribed and inductively analyzed using a grounded theory approach and two-coder system. All codes and themes were finalized once agreement was reached by both coders. Resultant themes from axial coding had to represent the voices of at least 80% of participants. Three characteristics emerged as being desired by college educators: brevity, clarity, and direction. Educators also expressed considerably less utility with medical documentation designed for pediatric students with concussion. College educators desire medical notes that are brief, clear, and provide straightforward direction, in addition to documentation that is tailored for the college setting.

Indexed as

collegeconcussionmedical notesreturn to learnreturn to school

Identifiers

PMID35558728
PMCPMC9080999

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.