Needs assessment and treatment compliance at state opioid substitution treatment programes in Georgia.
Bibliographic record
Abstract
AIMS: conduct needs assessments and treatment compliance evaluations in MMT and Suboxone Substitution State Programs in Georgia (Republic of). 506 patients (2 females) were surveyed (92% on Methadone, 8% on Suboxone) from 6 Tbilisi and 4 regional State Programs in 2011 November. Mean age - 40±8,56 (22-65) year; 254 (51.4%) were in treatment for 1-3 year. Evaluation was carried out on the base of structured self-questionnaire that covers demographics, drug use history, general drug use trends, psychotherapeutic sessions' acceptance and open label question regarding treatment challenges and satisfaction. 305 (60.3%) attended individual and 57 (11.3%) group psychotherapy sessions with 50.79% attending once/month or rare. The main reason given for therapy non-attendance - no needs for it (29.48%); the main drugs before admission - heroin (80.04%), buprenorphine (53.49%); Main drugs used in Georgia nowadays - desomorphine ("crocodile"), alcohol and marihuana. Commonly used drugs by program patients (136 positive answers) - alcohol-13.62%, marihuana-10.39%, pregabalin - 8.17%, opioids- 6.62% (mostly-"crocodile"), home-made stimulants-6.23%, sedatives -5.45%. 55.4% are extremely satisfied with treatment, 82.4% - with program staff. Patients' main wishes- free of charge programs (46.4%) and provide take-home doses (22.07%). Methadone and Suboxone ST are being well accepted in Georgia and appear to be reducing illegal opioid use. However, the psychotherapeutic sessions' attendance is very low.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".