Early Detection Model of Drug Abuse Relapse in the City of Padang
Bibliographic record
Abstract
The past year prevalence rate was 1.80% or 180 out of 10,000 Indonesians aged 15-64 years or equivalent to approximately 3.4 million people. The survey also found that drug abuse has penetrated into the countryside with very prominent drug use at a very productive age (25-49 years) and the prevalence rate in the past year of use above 2.5%. The various impacts of drug use can be overcome by conducting a rehabilitation program. The process of drug rehabilitation is a process given to drug addicts so that their mental, physical and social conditions improve, the existence of rehabilitation is expected to be able to reduce the adverse effects on physical and mental conditions and can reduce dependence and relapse due to drug use, so as to reduce the number of drug abusers. In this post-rehabilitation stage, drug abusers are prone to relapse. The case of relapse in drug users is very high, found in more than 50% of addicts in the last decade. Based on research, relapse rates are known to reach approximately 80 percent within the first six months, and occur as much as approximately 50 percent within two years. However, the various definitions of relapse have led to different relapse rates in Indonesia. The Ministry of Health in 2018 claimed that the relapse rate in Indonesia reached 24.3% while the relapse rate according to BNN stated that before the implementation of rehabilitation, Indonesia's relapse rate reached 90%. Indonesia's relapse rate after the implementation of rehabilitation at the Lido Bogor rehabilitation and therapy center is around 7%. Methods: This study uses a qualitative design with a phenomenological approach and aims to determine the determinants of early detection of relapse in drug abusers in Padang city. The informants in this study are drug abuser clients who are undergoing rehabilitation, in the post-rehabilitation program, and who have completed the rehabilitation program at HB Saanin Mental Hospital Padang, West Sumatera BNNP Clinic, and Yayasan Karunia Insani in Padang City, with a total of 6 people. In addition, the respondent sample consisted of 30 drug abusers who were undergoing rehabilitation. The analysis included instrument validity and reliability tests, expert analysis, and diagnostic test analysis. Results: Respondents' ages varied from 18 to 46 years old Factors that encourage relapse are the influence of friends and invitations from friends who use drugs. In addition, the absence of work and family problems also encourage relapse, Family, friends and community support for resilience, Informants revealed that rehabilitation programs can help informants from the risk of relapse, Informants confirmed that relapse can occur in anyone even in people undergoing intensive treatment, comprehensive and sustainable lecture programs can prevent relapse, Stress, depression, and social pressure factors affect the risk of relapse, the first signs of relapse felt by informants are unstable emotions. The developed relapse early detection model has significant predictive ability with an AUC of 78% and can predict the incidence of relapse with an accuracy between 60.2% and 95.8%. The model shows a strong correlation with the SSRS and has a 10,200 times greater chance of detecting relapse cases than the SSRS. Conclusion: Informants define relapse as a situation where someone who has used drugs uses drugs again, Factors that encourage relapse are the influence of friends and invitations from friends who use drugs. In addition, the absence of work and family problems also encourage relapse, Family, friends and community support for resilience, Informants revealed that rehabilitation programs can help informants from the risk of relapse, Informants confirmed that relapse can occur in anyone even in people undergoing intensive treatment, comprehensive and sustainable lecture programs can prevent relapse events, Stress, depression, and social pressure factors affect the risk of relapse, the first signs of relapse felt by informants are unstable emotions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".