Evidence map›Paper›PMID 35629119›Full record

ArticleJournal of personalized medicine2022

Towards a Didactic Concept for Heterogeneous Target Groups in Digital Learning Environments-First Course Implementation.

Matthias Katzensteiner, Stefan Vogel, Jens Hüsers, Jendrik Richter, Oliver J Bott

Abstract read
In one paragraph

Article in Journal of personalized medicine, 2022. 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.

Matthias KatzensteinerUniversity of Applied Sciences and Arts Hanover, 30539 Hannover, Germany.ORCID 0000-0002-0665-6110
Stefan VogelDepartment for Medical Informatics, University Medical Center Göttingen, 37075 Göttingen, Germany.ORCID 0000-0001-7729-4260
Jens HüsersHealth Informatics Research Group, University AS Osnabrück, 49076 Osnabrück, Germany.ORCID 0000-0003-3324-9155
Jendrik RichterDepartment for Medical Informatics, University Medical Center Göttingen, 37075 Göttingen, Germany.ORCID 0000-0002-3254-7380
Oliver J BottUniversity of Applied Sciences and Arts Hanover, 30539 Hannover, Germany.ORCID 0000-0002-8823-5247

Funding

Hanover University of Applied Sciences and Arts Library's Publications FundNiedersächsische Ministerium für Wissenschaft und Kultur ZN3491
6 · The paper itself

Abstract

In the context of the ongoing digitization of interdisciplinary subjects, the need for digital literacy is increasing in all areas of everyday life. Furthermore, communication between science and society is facing new challenges, not least since the COVID-19 pandemic. In order to deal with these challenges and to provide target-oriented online teaching, new educational concepts for the transfer of knowledge to society are necessary. In the transfer project "Zukunftslabor Gesundheit" (ZLG), a didactic concept for the creation of E-Learning classes was developed. A key factor for the didactic concept is addressing heterogeneous target groups to reach the broadest possible spectrum of participants. The concept has already been used for the creation of the first ZLG E-Learning courses. This article outlines the central elements of the developed didactic concept and addresses the creation of the ZLG courses. The courses created so far appeal to different target groups and convey diverse types of knowledge at different levels of difficulty.

Indexed as

citizenscommunicationdidacticdigitalizationdigitizationeducationE-Learninghealthcare communicationsHealthcare ITpatient empowerment

Identifiers

PMID35629119
PMCPMC9147950

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.