Scaling up a psychological intervention for alcohol misuse in wartime: a qualitative study in Ukraine
Bibliographic record
Abstract
Abstract Background The war in Ukraine has intensified mental health vulnerabilities, including alcohol misuse among conflict-affected men. The CHANGE intervention, a new transdiagnostic intervention building on WHO's Problem Management Plus (PM+), aims to address alcohol misuse and common mental disorders. This study explores the scalability of CHANGE within Ukraine's war-affected health system. Methods This qualitative study, guided by CFIR, involved online interviews with twenty stakeholders: 13 program implementers (i.e. providers), 2 adopters (i.e. organisations adopting CHANGE), and 5 maintainers (i.e. national health officials). Interview guides explored key barriers, facilitators, and implementation strategies for CHANGE in Ukraine. Results The key implementation barriers identified were: insufficient state support, a lack of primary care referrals, limited societal awareness of psychological interventions, scarce funding and challenges in integrating CHANGE into existing services. Conversely, implementers and adopters highlighted established partnerships with local and national organisations as crucial facilitators, alongside a supportive work environment, team professionalism, and recipient/deliverer centredness. The ongoing war impacts implementation by: fears of data confidentiality and mobilisation reduce participation, economic hardship limits access, and insecurity causes service disruption. Key facilitators include: online adaptation, enabling remote access, and increased attention from community organisations to society's mental health needs. Strategies suggested for CHANGE scale-up involve awareness-raising campaigns, building trust through community engagement and leveraging existing networks for effective outreach. Conclusions Implementation of a psychological intervention during war benefits from online adaptation for remote access and community engagement. Implementation strategies from CHANGE could inform global dissemination efforts in similar contexts. Key messages • By exploring implementation strategies of a psychological intervention amid active war, this study enhances the understanding of mental health service scalability in conflict-affected settings. • Research into implementation strategies is crucial to overcome war-related barriers during the deployment of psychological interventions like CHANGE.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".