Clinical and functioning outcomes during the establishment phase of Ukraine's community mental health teams: a descriptive analysis
Bibliographic record
Abstract
Background Ukraine's nationwide Community Mental Health Teams (CMHTs) programme is key to Ukraine's ongoing mental healthcare reform. No studies to date, however, have reported on the impact of Ukrainian CMHTs on service user clinical recovery. This study has two aims: (i) describe who the Ukrainian CMHTs are enrolling, which services they most provide, and where they are provided and (ii) identify whether any clinical and/or functional improvements were detectable among service users after six CMHT visits (intake + five follow-up visits) and, if so, identify principal predictors of such improvements. Methods 947 CMHT service users enrolled between April–December 2021 were assessed on clinical outcomes using the Clinical Global Improvement scale (CGI) and functional outcomes using WHO's Disability Assessment Schedule (WHODAS 2·0). Chi-square and Wilcoxon signed-rank tests were used to assess changes in CGI and WHODAS scores, respectively, at the fifth (or fourth) follow-up CMHT visit. Hierarchical multinomial logistic regression and hierarchical multiple linear regression identified predictors of clinical and functional improvement, respectively. Findings Most service users were male, unemployed, and diagnosed with schizophrenia spectrum disorders. Among service users with available outcome data at both CMHT intake and the fifth (or fourth) follow-up visit, a significant decrease in disability scores was observed (Median intake = 62·50, Median follow-up = 58·33, z = −6·27, p < 0·001) and most service users' illness severity stabilised (n = 451/742, 60·8%) or improved (n = 243/742, 32·6%). Clinical stabilisation (compared to worsening) was predicted by being male and living <20 km from the CMHT office, while improvement was predicted by frequent receipt of pharmacological support and receiving CMHT care in non-conflict-exposed regions. Functional improvement was predicted by living between 20 and 100 km from the CMHT office, having a somatic comorbidity, more frequent receipt of psychosocial services for the service user's family, and more support for community integration. Interpretation We found positive results associated with enrolment in Ukraine's CMHTs. Recommendations for future research and improvements to the CMHT programming are provided. Funding Funded as part of the World Health Organization's Special Initiative for Mental Health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".