Risk Factors for Non-Initiation and Dropout in Blended Therapy in Inpatient Psychiatric Patients - A Retrospective Cohort Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Blended therapy (BT) combines digital applications with face-to-face treatment and has become an increasingly important component of psychiatric care. Evidence indicates that BT can achieve outcomes comparable to or even superior to those of traditional face-to-face therapy. Despite certain advantages, routine implementation of BT remains challenging, and clinical practice suggests that while some inpatients engage with BT, many either discontinue early or do not initiate its use at all. To better understand these patterns, this multicentric, retrospective observational study investigates factors associated with noninitiation and dropout among inpatients who are offered BT. OBJECTIVE In this study, data from 278 inpatients were analyzed to examine the influence of sociodemographic variables, comorbidities, and symptom severity on the uptake and continued use of BT. The objective was to identify predictors of noninitiation and dropout. METHODS Multivariable logistic regression models were conducted to identify significant predictors of noninitiation and dropout among inpatients using the transdiagnostic, cognitive behavioral therapy–based electronic mental health platform Minddistrict, which offers modules targeting psychoeducation, cognitive restructuring, and behavioral activation. Data were collected from 2 psychiatric hospitals between January 2020 and May 2024. The sample consisted predominantly of patients diagnosed with depression (182/278, 65.7%) and posttraumatic stress disorder (61/278, 21.9%), alongside various comorbid conditions. RESULTS The findings indicate distinct patterns of association for noninitiation and dropout. Of the 278 patients, only 5 (1.8%) completed all the assigned modules, and one-third of the patients never initiated the platform at all. Specifically, increasing age was linked to a lower risk of noninitiation (odds ratio [per year age difference] 0.98, 95% CI 0.96-1.00; P=.01), while the presence of a comorbid anxiety disorder was associated with a reduced risk of dropout (odds ratio 0.23, 95% CI 0.08-0.66; P=.007). Several variables showed no association with either noninitiation or dropout across all analyses, including sex, overall symptom severity, and certain comorbidities such as personality disorders and depression. CONCLUSIONS In this preselected inpatient sample, uptake of BT was very limited. Older age was associated with lower noninitiation, and comorbid anxiety disorders were associated with a lower likelihood of dropout. These findings may help inform future prospective studies on how BT can be introduced and supported more effectively in inpatient psychiatric care. As access to BT was granted selectively by therapists, the results should be interpreted as predictors of engagement within a selected sample rather than general predictors of BT uptake among all psychiatric inpatients. CLINICALTRIAL
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".