A Meta-analysis of Internet-based Cognitive Behavioral Therapy for Military and Veteran Populations
Bibliographic record
Abstract
Abstract Background Military and veteran populations are unique in their trauma exposures, rates of mental illness and comorbidities, and response to treatments. While reviews have suggested that internet-based Cognitive Behavioral Therapy (iCBT) can be useful for treating mental health conditions, the extent to which they may be appropriate for military and veteran populations remain unclear. The goals of the current meta-analysis are to: (1) substantiate the effects of iCBT for military and veteran populations, (2) evaluate its effectiveness compared to control conditions, and (3) examine potential factors that may influence their effectiveness. Methods This review was completed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting and Cochrane review guidelines. The literature search was conducted using PsycInfo, Medline, Embase, and Proquest Dissertation & Theses on June 4, 2021 with no date restriction. Inclusion criteria included studies that: (1) were restricted to adult military or veteran populations, (2) incorporated iCBT as the primary treatment, and (3) evaluated mental health outcomes. Exclusion criteria included: (1) literature reviews, (2) qualitative studies, (3) study protocols, (4) studies that did not include a clinical/analogue population, and (5) studies with no measure of change on outcome variables. Two independent screeners reviewed studies for eligibility. Data was pooled and analyzed using random-effects and mixed-effects models. Study data information were extracted as the main outcomes, including study condition, sample size, and pre- and post-treatment means and standard deviations for all assessed outcomes. Moderator information were also extracted, and included demographics information, the types of outcomes measured, primary condition targeted, concurrent treatment, drop pout rate, and format, length, and delivery of intervention. Results A total of 20 studies and 144 samples of data were included in the meta-analysis. The pooled effect size showed a small but meaningful effect for iCBT, g = 0.63, SE = 0.05, 95% CI (0.54, 0.73), Z = 13.14, p < .001. Conclusions Findings from the meta-analysis lend support for the use of iCBT with military and veteran populations. Conditions under which iCBT may be optimized are discussed.
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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 | 0.029 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.070 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".