The Impact of Couple Therapy on Service Utilization among Military Veterans: The Moderating Roles of Pretreatment Service Utilization and Premature Termination
Bibliographic record
Abstract
Couple therapy reduces relational and individual distress and may affect utilization of other health services, particularly among higher service utilizers. Although average decreases in service utilization are predicted among recipients of couple therapy, low utilizers of services may appropriately increase use. The relationship between couple therapy and service utilization was examined among a sample of 179 U.S. military veterans who received treatment in Veterans Affairs (VA) specialty couple therapy clinics. Consistent with hypotheses, overall mental and physical health visits decreased from the 12 months preceding couple therapy to the 12 months following treatment. Moderator analyses showed that decreases were greatest among individuals who were rated by their therapist as having completed a full course of couple therapy, suggesting that change was attributable to intervention. Pretreatment service utilization also moderated observed change-higher utilizers' use of services decreased substantially, whereas lower utilizers' slightly increased. Cost analyses revealed that the estimated per person mean cost in our sample decreased by $930.33 in the year following compared to the year prior to couple therapy, as per 2008 VA cost data. As service utilization data were only available for one partner and only for 1 year posttherapy, the true magnitude of this effect may be underestimated. Our findings are relevant to policy makers as they demonstrate that couple therapy reduces average service utilization and associated costs and addresses calls for analyses of cost effectiveness of systemic interventions.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".