Evidence map›Paper›PMID 38529012›Full record

ArticleJournal of offender rehabilitation2023

Implementation Preparation Costs of Virtual Reality Job Interview Training in Prisons: A Budget Impact Analysis.

Elizabeth C Danielson, Matthew J Smith, Brittany Ross, Brittani Parham, Jennifer E Johnson, Gary S Cuddeback, Justin D Smith, Dean McGregor, Aaron Suganuma, Neil Jordan

Abstract read
In one paragraph

Article in Journal of offender rehabilitation, 2023. 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
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.

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

10 authors.

Elizabeth C DanielsonDepartment of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL.
Matthew J SmithSchool of Social Work, University of Michigan, Ann Arbor, MI.
Brittany RossSchool of Social Work, University of Michigan, Ann Arbor, MI.
Brittani ParhamSchool of Social Work, University of Michigan, Ann Arbor, MI.
Jennifer E JohnsonDivision of Public Health, College of Human Medicine, Michigan State University, Flint, MI.
Gary S CuddebackSchool of Social Work, Virginia Commonwealth University, Richmond, VA.
Justin D SmithDepartment of Population Health Sciences, University of Utah School of Medicine, Salt Lake City, UT.
Dean McGregorMichigan Department of Corrections, Lansing, MI.
Aaron SuganumaWashtenaw County Sheriff's Office, Ann Arbor, MI.
Neil JordanCenter for Education in Health Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL.

Funding

ACL HHS 90ARHF0003
6 · The paper itself

Abstract

Virtual Reality Job Interview Training (VR-JIT) has increased employment rates for returning citizens when added to a successful prison-based employment readiness program. However, implementation preparation cost-expenses prior to offering VR-JIT to intended recipients-is unknown. We estimated the cost of implementation preparation activities (e.g., organizing workflow) for two prisons to deliver VR-JIT. We conducted a budget impact analysis and enumerated the labor costs incurred during this important stage of implementation. Labor costs were approximately $8,847 per prison. Our sensitivity analysis estimated the labor costs to replicate this effort in a new prison to range from $2,877 to $4,306 per prison. Thus, VR-JIT may be an affordable tool for prison-based employment readiness programs to improve gainful employment.

Indexed as

budget impact analysisimplementation preparationrecidivismvirtual reality job interview training

Identifiers

PMID38529012
PMCPMC10961941

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.