Leveraging AI in Drug and Substance Abuse Recovery: A Systematic Approach to Reintegration and Rehabilitation for the Homeless
Bibliographic record
Abstract
Integrating artificial intelligence (AI) into drug abuse recovery programs offers a promising avenue for enhancing rehabilitation and reintegration efforts, particularly among homeless populations. This study aims to explore the application of AI-driven interventions in supporting substance abuse recovery and facilitating the reintegration of homeless individuals into society. Adopting a qualitative methodology, a systematic literature review and case study analysis were conducted to evaluate the effectiveness of AI interventions in rehabilitation programs across different settings. The findings suggest that AI can significantly improve treatment outcomes by tailoring interventions to individual needs, predicting relapse risks, and optimizing service delivery. However, the implementation of AI in this context also raises ethical considerations, including data privacy, potential biases in algorithmic decision-making, and the need for equitable access to technology-based interventions. This study recommends that policymakers, healthcare providers, and social service organizations implement targeted digital literacy programs, ensure accessibility, continuous research, and establish regulatory frameworks to safeguard ethical AI usage. This study concludes that, with careful application and oversight, AI has the potential to transform drug abuse recovery and social reintegration processes for homeless individuals, leading to more effective and efficient support systems.
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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 | 0.098 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.007 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".