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Accepted for/Published in: JMIR Mental Health

Date Submitted: Aug 8, 2022
Date Accepted: Oct 20, 2022

The final, peer-reviewed published version of this preprint can be found here:

Training Staff Across the Veterans Affairs Health Care System to Use Mobile Mental Health Apps: A National Quality Improvement Project

McGee-Vincent P, Mackintosh MA, Jamison AL, Juhasz K, Becket-Davenport C, Bosch J, Avery TJ, Glamb LJ, Hampole S

Training Staff Across the Veterans Affairs Health Care System to Use Mobile Mental Health Apps: A National Quality Improvement Project

JMIR Ment Health 2023;10:e41773

DOI: 10.2196/41773

PMID: 36633895

PMCID: 9880807

Training staff across the VA healthcare system to use mobile mental health apps: A national quality improvement project

  • Pearl McGee-Vincent; 
  • Margaret-Anne Mackintosh; 
  • Andrea L. Jamison; 
  • Katherine Juhasz; 
  • Colleen Becket-Davenport; 
  • Jeane Bosch; 
  • Timothy J. Avery; 
  • Lauren J. Glamb; 
  • Shilpa Hampole

ABSTRACT

Background:

The National Center for PTSD, within the Department of Veterans Affairs (VA), has developed a suite of free, publicly available, evidence-informed apps that have the capacity to reach an increasing number of Veterans and bridge gaps in care by providing resources to those who are not engaged in mental health treatment. To expand reach of these apps, staff across VA service lines learned about these apps, their features and limitations, and how to introduce them to Veterans.

Objective:

Develop, disseminate, and evaluate a training for multidisciplinary staff as part of a national quality improvement project to increase the reach of mobile mental health apps as a resource for Veterans.

Methods:

Sites from all of VA’s 18 geographic regions enrolled in the project. At each site, a minimum of 25 VA staff who have direct contact with Veterans, including staff from the mental health service line and all other service lines, were recruited to participate. Training included a 3-hour multidisciplinary “core” module, and a 1-hour “clinical integration” module designed specifically for mental health clinicians. Due to the COVID-19 pandemic, the trainings were adapted to a virtual format. Pre- and post-training surveys assessed program reach (i.e., participants enrolled per site), satisfaction, and effectiveness of trainings, as measured by changes in knowledge, basic skills, and behavioral intentions to use apps with Veterans.

Results:

A total of 1,110 participants representing 33 disciplines at 19 VA sites completed the training. 67.7% of participants were mental health staff. Sites averaged 58.4 participants (SD = 36.49, median = 51). Most participants were satisfied with the training (93.8%) and reported that they would recommend the training to others (92.4%). App knowledge scores significantly increased from pre-training (M = 80.8%, SD = 15.77%) to post-training (M = 91.1%, SD = 9.57%), p < .001. At post-training, participants also reported greater confidence in their ability to show Veterans how to download (Z = -13.86, P < .001) and use VA MH apps (Z = -15.13, P < .001). There was near universal endorsement by staff for their intentions to recommend apps to Veterans as well as their ability to identify at least one specific Veteran to whom they could recommend an app. Staff also reported strong motivation to encourage other VA staff to share apps with Veterans.

Conclusions:

The training far exceeded the initial goals for staff recruitment and training in all three metrics. 32.3% of participants came from service lines outside of mental health, indicating the feasibility of introducing these mental health resources during medical appointments and other contexts.


 Citation

Please cite as:

McGee-Vincent P, Mackintosh MA, Jamison AL, Juhasz K, Becket-Davenport C, Bosch J, Avery TJ, Glamb LJ, Hampole S

Training Staff Across the Veterans Affairs Health Care System to Use Mobile Mental Health Apps: A National Quality Improvement Project

JMIR Ment Health 2023;10:e41773

DOI: 10.2196/41773

PMID: 36633895

PMCID: 9880807

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© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.