Evidence map›Paper›PMID 38089207›Full record

ArticleMedEdPublish (2016)2018

Turk Talk: human-machine hybrid virtual scenarios for professional education.

Michelle Cullen, Nishan Sharma, David Topps

Open access · goldAbstract read
In one paragraph

Article in MedEdPublish (2016), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 81% of its field
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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

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

3 authors at 1 institution in 1 country.

Michelle CullenUniversity of Calgary.
Nishan SharmaUniversity of Calgary.
David ToppsUniversity of Calgary.
University of Calgary · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article was migrated. The article was marked as recommended. Virtual scenarios provide a means for creating rich and complex online cases for health professional students to explore. However, the response options available to the learner are usually predefined, which limits the utility of virtual patients. Using artificial intelligence or natural language processing to accommodate such flexibility is expensive and hard to design. This project description lays out an alternative approach to making virtual scenarios more adaptable and interactive. Using OpenLabyrinth, an open-source educational research platform, we modified the interface and functionality to provide a human-computer hybrid interface, where a human facilitator can interact with learners from within the online case scenario. Using a design-based research approach, we have iteratively improved our cases, workflows and scripts and interface designs. The next step is testing this new functionality in a variety of situations. This report describes the pilot implementation of this pilot project. It includes the background, rationale, objectives, learning and educational designs, and implications for software development. The costs and time required to modify the software were much lower than anticipated. Facilitators managed text input from multiple concurrent learners. Learners noted a delay while waiting for the facilitator's response, but denied becoming frustrated. The implementation and use of this new technique seems promising for training and assessment purposes related to developing effective communication skills. This report also explores the provisional implications arising from the study so far.

Indexed as

nursing educationtext-based chattherapeutic communicationvirtual scenarios

Identifiers

PMID38089207
PMCPMC10711943
OpenAlexW2901484255

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.