MétaCan
Menu
Back to cohort
Record W3046702291 · doi:10.2196/22079

Virtual Trauma-Focused Therapy for Military Members, Veterans, and Public Safety Personnel With Posttraumatic Stress Injury: Systematic Scoping Review

2020· article· en· W3046702291 on OpenAlexaffvenue
Chelsea Jones, Antonio Miguel Cruz, Lorraine Smith‐MacDonald, Emily Cruikshank, Delaram Baghoori, Avneet Kaur Chohan, Alexa Laidlaw, Allison White, Bo Cao, Vincent I. O. Agyapong, Lisa Burback, Olga Winkler, Phillip R. Sevigny, Liz Dennett, Martin Ferguson-Pell, Andrew J. Greenshaw, Suzette Brémault‐Phillips

Bibliographic record

VenueJMIR mhealth and uhealth · 2020
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsGlenrose Rehabilitation HospitalCanadian Armed ForcesUniversity of Alberta
Fundersnot available
KeywordsPosttraumatic stressMilitary personnelPsychologyMedicineClinical psychologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: A necessary shift from in-person to remote delivery of psychotherapy (eg, teletherapy, eHealth, videoconferencing) has occurred because of the COVID-19 pandemic. A corollary benefit is a potential fit in terms of the need for equitable and timely access to mental health services in remote and rural locations. Owing to COVID-19, there may be an increase in the demand for timely, virtual delivery of services among trauma-affected populations, including public safety personnel (PSP; eg, paramedics, police, fire, correctional officers), military members, and veterans. There is a lack of evidence on the question of whether digital delivery of trauma-therapies for military members, veterans, and PSP leads to similar outcomes to in-person delivery. Information on barriers and facilitators and recommendations regarding digital-delivery is also scarce. OBJECTIVE: This study aims to evaluate the scope and quality of peer-reviewed literature on psychotherapeutic digital health interventions delivered remotely to military members, veterans, and PSP and synthesize the knowledge of needs, gaps, barriers to, and facilitators for virtual assessment of and virtual interventions for posttraumatic stress injury. METHODS: Relevant studies were identified using MEDLINE (Medical Literature Analysis and Retrieval System Online), EMBASE (Excerpta Medica dataBASE), APA (American Psychological Association) PsycINFO, CINAHL (Cumulative Index of Nursing and Allied Health Literature) Plus with Full Text, and Military & Government Collection. For collation, analysis, summarizing, and reporting of results, we used the CASP (Critical Skills Appraisal Program) qualitative checklist, PEDro (Physiotherapy Evidence Database) scale, level of evidence hierarchy, PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews), and narrative synthesis. RESULTS: A total of 38 studies were included in this review. Evidence for the effectiveness of digital delivery of prolonged exposure therapy, cognitive processing therapy, behavioral activation treatment with therapeutic exposure to military members, veterans, and PSP was rated level 1a, whereas evidence for cognitive behavioral therapy was conflicting. The narrative synthesis indicated that virtual delivery of these therapies can be as effective as in-person delivery but may reduce stigma and cost while increasing access to therapy. Issues of risk, safety, potential harm (ie, suicidality, enabling avoidance), privacy, security, and the match among the therapist, modality, and patient warrant further consideration. There is a lack of studies on the influences of gender, racial, and cultural factors that may result in differential outcomes, preferences, and/or needs. An investigation into other therapies that may be suitable for digital delivery is needed. CONCLUSIONS: Digital delivery of trauma therapies for military members, veterans, and PSP is a critical area for further research. Although promising evidence exists regarding the effectiveness of digital health within these populations, many questions remain, and a cautious approach to more widespread implementation is warranted.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.100
GPT teacher head0.401
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreEmpirical

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".

Quick stats

Citations62
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueJMIR mhealth and uhealthSame topicTelemedicine and Telehealth ImplementationFrench-language works237,207