Supporting the Mental Health Needs of Military Partners Through the Together Webinar Program: Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Despite an increased risk of psychological difficulties, there remains a lack of evidence-based support for the mental health needs of military partners. OBJECTIVE: This study aims to investigate whether the Together Webinar Programme (TTP-Webinar), a 6-week structured, remote access group intervention would reduce military partners' experience of common mental health difficulties and secondary trauma symptoms. METHODS: A pilot randomized controlled trial was used to compare the TTP-Webinar intervention with a waitlist control. The sample was UK treatment-seeking veterans engaged in a mental health charity. A total of 196 military partners (1 male and 195 females; aged mean 42.28, SD 10.82 years) were randomly allocated to the intervention (n=97) or waitlist (n=99) condition. Outcome measures were self-reported measures of common mental health difficulties, secondary trauma symptoms, and overall quality of life rating. RESULTS: Compared with the waitlist, military partners in the TTP-Webinar had reduced common mental health difficulties (P=.02) and secondary trauma symptoms (P=.001). However, there was no difference in quality-of-life ratings (P=.06). CONCLUSIONS: The results suggest that TTP-Webinar is an effective intervention to support the mental health difficulties of military partners. This study provides promising evidence that webinars may be an appropriate platform for providing group-based support. TRIAL REGISTRATION: ClinicalTrials.gov NCT05013398; https://clinicaltrials.gov/ct2/show/NCT05013398.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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.
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".