Three Months of Text4Hope-Addiction Support Program mitigates substance craving and improves mental health.
Bibliographic record
Abstract
Introduction Problematic substance use is rising, and other mental health conditions like anxiety and depression correlate with substance abuse. Diverse interventions to reduce this effect are emerging. Supportive text messages offer the prospect of improving symptoms of drug misuse and other associated comorbidities. Objectives The study aims to evaluate the impact of the Text4Hope-Addiction program in mitigating craving, anxiety, and depression symptoms in subscribers. Methods Individuals self-subscribe to Text4Hope Addiction program by texting “Open2Change” to 393939 to receive daily addiction-related text messages for three months. Subscribers are invited via text message to complete online questionnaires which assess cravings, anxiety, and depressive symptoms using the Brief Substance Craving Scale, Generalized Anxiety Disorder-7 Scale, and Patient Health Questionnaire-9 on subscription (baseline), six weeks and three months. Data were analyzed using SPSS version 25 with descriptive and inferential statistics. Satisfaction responses were used to assess various aspects of the Text4Hope- Addiction program. Results There was a significant difference in the mean baseline and three-month BSCS scores ( -2.17, 95% CI of -0.62 to -3.72), PHQ-9 scores (-5.08, 95% CI of -1.65 to -8.51), and the GAD-7 scores (-2.93, 95% CI of -0.48 to -5.56). Participants agreed that the supportive text messages helped them cope with addiction-related stress (89%), anxiety (81%) and depression (69%). Conclusions The Text4Hope-Addiction program effectively reduced cravings, anxiety, and depression among subscribers, with high satisfaction rates for the program. Healthcare practitioners and policymakers should consider implementing supportive text-based strategies to complement conventional treatments for addiction. Disclosure of Interest None Declared
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".