Quality of Life among Methadone Maintenance Patients after the 4th Wave of COVID-19 in Ho Chi Minh City, Vietnam
Bibliographic record
Abstract
INTRODUCTION: This study aimed to assess the quality of life (QOL) of methadone maintenance patients in Ho Chi Minh City after the most devastating wave of COVID-19 and to explore factors that influence their QOL. METHODS: A cross-sectional study was conducted on 230 people who were in a methadone maintenance phase at the Tan Binh methadone treatment clinic using WHOQoL-BREF questionnaires. Ten in-depth interviews were carried out with patients and health staff, who were purposely selected. RESULTS: The overall QOL score of study participants according to the 100-point WHOQoL-BREF scale was 64.6 ± 9.8, in which the highest average score was the physical health domain (68.3 ± 11.1 points) and the lowest was the social relationship domain (59.1 ± 13.5 points). MMT patients’ employment was found to be strongly affected by the COVID-19 epidemic, with higher unemployment or unstable jobs that negatively influence their QOL. In contrast, the take-home dose policy applied during lockdown was reported as a positive factor and well accepted. Family support and marriage also positively affected their QOL scores, whereas those with positive urine test results reported lower QOL scores. CONCLUSION: Employment and social support for MMT patients has emerged, and further studies should be carried out to provide adequate evidence for methadone treatment improvement, including a multi-day take-home dose initiative.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".