The Experience of Key Stakeholders During the Implementation and Use of Trauma Therapy via Digital Health for Military, Veteran, and Public Safety Personnel: Qualitative Thematic Analysis (Preprint)
Bibliographic record
Abstract
BACKGROUND Exposure to occupational stressors and potentially psychologically traumatic events experienced by public safety personnel (eg, paramedics, police, fire, and correctional officers), military members, and veterans can lead to the development of posttraumatic stress injuries and other mental health disorders. Providing emergency services during COVID-19 has intensified the challenges. Owing to COVID-19 restrictions, mental health service providers offering support to these populations have had to rapidly pivot to use digital versus in-person methods of service delivery. OBJECTIVE This paper aims to explore the experience of mental health service providers regarding digital health service delivery, including the current state of digital mental health service delivery, barriers to and facilitators of the use of digital health for mental health service delivery experienced during the pandemic, and recommendations for implementing and integrating digital health into regular mental health service delivery. METHODS This embedded mixed-methods study included questionnaires and focus groups with key stakeholders (N=31) with knowledge and experience in providing mental health services. Data analysis included descriptive, quantitative, and qualitative thematic analyses. RESULTS The following three themes emerged: being forced into change, daring to deliver mental health services using digital health, and future possibilities offered by digital health. In each theme, participants’ responses reflected their perceptions of service providers, organizations, and clients. The findings offer considerations regarding for whom and at what point in treatment digital health delivery is appropriate; recommendations for training, support, resources, and guidelines for digitally delivering trauma therapy; and a better understanding of factors influencing mental health service providers’ perceptions and acceptance of digital health for mental health service delivery. CONCLUSIONS The results indicate the implementation of digital health for mental health service delivery to military members, public safety personnel, and veterans. As the COVID-19 pandemic continues, remote service delivery methods for trauma therapy are urgently needed to support the well-being of those who have served and continue to serve. CLINICALTRIAL
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".