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Record W4285042064 · doi:10.2196/33682

The Redesign and Validation of Multimodal Motion-Assisted Memory Desensitization and Reconsolidation Hardware and Software: Mixed Methods, Modified Delphi–Based Validation Study

2022· article· en· W4285042064 on OpenAlexafffundvenueabout
Chelsea Jones, Lorraine Smith‐MacDonald, Matthew Brown, Jacob VanDehy, Rasmus Grunnet-Jepsen, Vrajeshri P Ordek, Sarah E. Kruger, Anne Ayres Gerhart, Nancy van Veelen, Mirjam J. Nijdam, Lisa Burback, Bo Cao, Michael J. Roy, Pinata H. Sessoms, Eric Vermetten, Suzette Brémault‐Phillips

Bibliographic record

VenueJMIR Human Factors · 2022
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsUniversity of AlbertaAlberta Health Services
FundersBureau of Medicine and SurgeryU.S. NavyCanadian Armed ForcesGovernment of CanadaU.S. Department of DefenseGlenrose Rehabilitation HospitalAlberta Health ServicesGovernment of AlbertaU.S. Department of Veterans Affairs
KeywordsDelphi methodIntervention (counseling)Memory consolidationComputer scienceSoftwareDesensitization (medicine)PsychologyApplied psychologyMedicineMedical educationNursingArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: In recent years, the delivery of evidence-based therapies targeting posttraumatic stress disorder (PTSD) has been the focus of the Departments of Defense in countries such as Canada, the Netherlands, and the United States. More than 66% of military members continue to experience symptoms of PTSD that significantly impact their daily functioning and quality of life after completing evidence-based treatments. Innovative, engaging, and effective treatments for PTSD are needed. Multimodal motion-assisted memory desensitization and reconsolidation (3MDR) is an exposure-based, virtual reality-supported therapy used to treat military members and veterans with treatment-resistant PTSD. Given the demonstrated efficacy of 3MDR in recently published randomized control trials, there is both an interest in and a need to adapt the intervention to other populations affected by trauma and to improve accessibility to the treatment. OBJECTIVE: We aimed to further innovate, develop, and validate new and existing hardware and software components of 3MDR to enhance its mobility, accessibility, feasibility, and applicability to other populations affected by trauma, including public safety personnel (PSP), via international collaboration. METHODS: This study used a modified Delphi expert consultation method and mixed methods quasi-experimental validation with the purpose of software validation among PSP (first responders, health care providers) participants (N=35). A team of international experts from the Netherlands, the United States, and Canada met on the web on a weekly basis since September 2020 to discuss the adoption of 3MDR in real-world contexts, hardware and software development, and software validation. The evolution of 3MDR hardware and software was undertaken followed by a mixed methods software validation study with triangulation of results to inform the further development of 3MDR. RESULTS: This study resulted in the identification, description, and evolution of hardware and software components and the development of new 3MDR software. Within the software validation, PSP participants widely acknowledged that the newly developed 3MDR software would be applicable and feasible for PSP affected by trauma within their professions. The key themes that emerged from the thematic analysis among the PSP included the desire for occupationally tailored environments, individually tailored immersion, and the applicability of 3MDR beyond military populations. CONCLUSIONS: Within the modified Delphi consultation and software validation study, support for 3MDR as an intervention was communicated. PSP participants perceived that 3MDR was relevant for populations affected by trauma beyond military members and veterans. The resulting hardware and software evolution addressed the recommendations and themes that arose from PSP participants. 3MDR is a novel, structured, exposure-based, virtual reality-supported therapy that is currently used to treat military members and veterans with PTSD. Going forward, it is necessary to innovate and adapt 3MDR, as well as other trauma interventions, to increase effectiveness, accessibility, cost-effectiveness, and efficacy among other populations affected by trauma.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.124
GPT teacher head0.419
Teacher spread0.295 · 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 designObservational
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

Citations7
Published2022
Admission routes4
Has abstractyes

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