Supplementing the Interference Preservation Hypothesis With Motivational Interviewing-Based Feedback in a Rural Military Veteran With Moral Injury: Formative Case Study.
5 affiliations
- 1School of Human Sciences, Southern Illinois University Carbondale, 475 Clocktower Drive, Carbondale, IL, 62901, United States, 1 6184531865.
- 2James A. Haley Veterans' Hospital, Tampa, FL, United States.
- 3Division of Epidemiology, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, United States.
- 4Department of Emergency Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, United States.
- 5Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.
Abstract
Background
Veterans experiencing moral injury (ie, having witnessed or done something during military service that conflicts with one's value system) may encounter challenges that affect engagement and learning in online contexts.Objective
This formative case study examined the feasibility of integrating motivational interviewing (MI)-based delayed feedback, a key tenet of the interference preservation hypothesis, into an online program focused on machine learning instruction for a veteran with self-reported probable moral injury.Methods
One veteran completed a moral injury assessment and 17 training modules with various checkpoint assessments, followed by a machine learning module incorporating a pretest, MI-based delayed feedback, and a posttest.Results
The participant completed all instructional and assessment components. Checkpoint scores ranged from 66.7% to 100%, with 7 of 10 checkpoints exhibiting a perfect score. Machine learning performance increased from 90% at pretest to 100% at posttest following MI-based delayed feedback.Conclusions
These findings demonstrate the feasibility of integrating MI-based delayed feedback within an online data science instruction program and provide formative evidence supporting evaluation in larger samples.
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BibTeX
@article{mcdaniel2026supplementing,
title = {{Supplementing the Interference Preservation Hypothesis With Motivational Interviewing-Based Feedback in a Rural Military Veteran With Moral Injury: Formative Case Study.}},
author = {Justin T McDaniel and Jolie N Haun and Jenna Jamieson and Rebecca Campbell-Montalvo and Dawn Roberts and Lenorris Hughes and Dustin D French},
journal = {JMIR formative research},
year = {2026},
month = oct,
doi = {10.2196/101294},
url = {https://europepmc.org/article/MED/42953928},
note = {CC BY},
}
RIS
TY - JOUR
TI - Supplementing the Interference Preservation Hypothesis With Motivational Interviewing-Based Feedback in a Rural Military Veteran With Moral Injury: Formative Case Study.
AU - Justin T McDaniel
AU - Jolie N Haun
AU - Jenna Jamieson
AU - Rebecca Campbell-Montalvo
AU - Dawn Roberts
AU - Lenorris Hughes
AU - Dustin D French
PY - 2026
DA - 2026/10/22
JO - JMIR formative research
DO - 10.2196/101294
UR - https://europepmc.org/article/MED/42953928
AB - Background Veterans experiencing moral injury (ie, having witnessed or done something during military service that conflicts with one's value system) may encounter challenges that affect engagement and learning in online contexts.Objective This formative case study examined the feasibility of integrating motivational interviewing (MI)-based delayed feedback, a key tenet of the interference preservation hypothesis, into an online program focused on machine learning instruction for a veteran with self-reported probable moral injury.Methods One veteran completed a moral injury assessment and 17 training modules with various checkpoint assessments, followed by a machine learning module incorporating a pretest, MI-based delayed feedback, and a posttest.Results The participant completed all instructional and assessment components. Checkpoint scores ranged from 66.7% to 100%, with 7 of 10 checkpoints exhibiting a perfect score. Machine learning performance increased from 90% at pretest to 100% at posttest following MI-based delayed feedback.Conclusions These findings demonstrate the feasibility of integrating MI-based delayed feedback within an online data science instruction program and provide formative evidence supporting evaluation in larger samples.
SN - 2561-326X
ER -