Evidence map›Paper›PMID 33967354›Full record

ArticleClinical social work journal2021

Using Evidence-Based Learning Theories to Guide the Development of Virtual Simulations.

Chad McDonald, Matt Davis, Cole Benson

Abstract read
In one paragraph

Article in Clinical social work journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  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

3 authors.

Chad McDonaldSocial Research Institute, University of Utah, 395 S. 1500 E., Salt Lake City, UT 84112 USA.ORCID 0000-0001-7495-3646
Matt DavisDivision of Health System Innovation & Research (HSIR), Department of Population Health Sciences, University of Utah, Williams Building, Rm 1N410, 295 Chipeta Way, Salt Lake City, UT 84108 USA.ORCID 0000-0002-6273-9279
Cole BensonSocial Research Institute, University of Utah, 395 S. 1500 E., Salt Lake City, UT 84112 USA.ORCID 0000-0002-4456-142X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Technology-enhanced training such as virtual simulations can be effective only to the extent evidence-based principles of learning are integrated into their training platforms. Assuming skill acquisition is the target of training, programs should include time and space for repeated practice opportunities structured through evidence-based learning theories (Amodeo et al. in Adm Soc Work 33:423-438, 2009). Essential learning attributes derived from Cognitive Load Theory (Sweller in Cognit Sci 12:257-285, 1988) and the Theory of Deliberate Practice (Ericsson et al. in Psychol Rev 100:363-406, 1993) can effectively integrate into virtual simulations intended to build expertise. Virtual Home Simulation (VHS) and Virtual Motivational Interviewing (VMI) are presented in this paper as an approach to develop virtual simulations that provide child welfare workers a means to deliberately practice essential skills toward competence before entering the workforce. This paper reviews the development process, specific design aspects, and lessons learned as a guide on how to integrate effective learning attributes. Implications for using virtual simulations, including cost-benefits, measuring performance over time, and addressing training complications due to Covid-19 or similar obstacles, are also provided.

Indexed as

Child welfareDeliberate practiceExpertiseSimulationTrainingVirtual reality

Identifiers

PMID33967354
PMCPMC8090927

What OpenQuestion holds

Textmetadata
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.