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Record W4312477273 · doi:10.2196/40145

Web-Based Mind-Body Tactical Resilience Training Program for First Responders: Pre-Post Study Assessing Feasibility, Acceptability, and Usability

2022· article· en· W4312477273 on OpenAlexvenueno aff
Leona Tan, Mark Deady, Olivia Mead, Rebecca M. Foright, Eric M Brenneman, Jamie R. Yeager, Richard A. Bryant, Samuel B. Harvey

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilMedical Research CouncilAustralian Government
KeywordsUsabilityMindsetPreparednessMental healthWeb applicationPsychologyCognitionPsychological resilienceMedicineApplied psychologyClinical psychologyPsychiatryWorld Wide WebComputer sciencePsychotherapist

Abstract

fetched live from OpenAlex

Background First responders report elevated rates of mental disorders, including posttraumatic stress disorder (PTSD), yet many are reluctant to seek care. Preventative resilience training programs attempt to proactively address this issue, and there is evidence showing promise for programs targeting cognitive processes. However, these programs rarely address the physical health conditions associated with PTSD. There is emerging evidence of mind-body exercise training improving PTSD symptoms as well as its associated physical health symptoms. However, the feasibility and acceptability of delivering a web-based mind-body resilience training among first responders are not yet known. Objective This study aimed to evaluate the feasibility, usability, and acceptability of a web-based mind-body tactical resilience training program designed for first responders. In addition, we explored the preliminary effectiveness of the training program on mental health outcomes, adaptive cognitive strategies, and work productivity. Methods A total of 42 first responders based in the United States enrolled in the web-based training program. Participants were administered web-based surveys before enrolling in the 6-week web-based program and at the end of the program. The primary outcomes of feasibility were measured using the number of training hours, program adherence rates, and self-reported data on frequency of practice. Acceptability and usability were measured using self-reported data. Secondary outcomes were symptoms of PTSD, psychological distress, emotion regulation, stress mindset, psychological preparedness, and work performance. Results Overall, the training program was feasible based on the median number of training hours spent on the web-based program (7.57 hours out of an expected total of 6 to 9 hours), and 55% (23/42) of the enrolled participants completed more than half of the program. Although acceptability, usability, and frequency of practice were rated as high, this was based on only 29% (12/42) of the respondents who provided follow-up data. Secondary outcomes showed a significant improvement in the adaptive cognitive strategy of the stress mindset, with a mean difference of –5.42 (SD 4.81; 95% CI −8.475 to −2.358; t11=−3.898; P=.002). All other secondary outcomes were not significant. However, the secondary outcomes were exploratory only, and this study was neither designed nor powered to adequately assess efficacy. Conclusions These findings suggest that a mind-body tactical resilience training program delivered in a web-based format is feasible and acceptable among first responders; however, further refinements may be required to improve adherence rates. Further research using a larger, more rigorous trial design is warranted to examine the effectiveness of this type of training as a possible prevention or treatment strategy for this population.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.240
GPT teacher head0.553
Teacher spread0.313 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2022
Admission routes1
Has abstractyes

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