Implementation and Evaluation of a Text Message–Based Addiction Counseling Program (Text4Hope-Addiction Support): Protocol for a Questionnaire Study (Preprint)
Bibliographic record
Abstract
BACKGROUND With the emergence of the COVID-19 pandemic, providing counseling to people with drug or alcohol addiction while maintaining physical distance has been challenging. This protocol describes the use of text messaging (as used in the Text4Hope-Addiction Support program) as a convenient, evidence-based, cost-effective, and accessible population-level mental health intervention with high user satisfaction proven in prior research. OBJECTIVE The project goal is to implement a program of daily supportive text messaging (Text4Hope-Addiction Support) to reduce drug or alcohol cravings as well as anxiety and depression, typically associated with alcohol and substance use disorders. The aim of this study is to evaluate the prevalence of cravings, anxiety, and depressive symptoms; demographic correlates of the same; and the outcomes of the Text4Hope-Addiction Support intervention in mitigating cravings, anxiety, and depressive symptoms. METHODS Self-administered, anonymous, online questionnaires will be used to assess cravings for the primary substance of addiction (Brief Substance Craving Scale), anxiety (Generalized Anxiety Disorder-7), and depressive symptoms (Patient Health Questionnaire-9). Data will be collected at baseline (onset of receiving text messages), program midpoint (6 weeks), and program end (12 weeks). RESULTS As of October 2020, data collection is in progress; and it is expected to be completed by fall 2021. Data analysis will include parametric and nonparametric techniques, focusing on primary outcomes (ie, cravings, anxiety, and depressive symptoms) and metrics of use, including the number of subscribers and user satisfaction. CONCLUSIONS This Text4Hope-Addiction Support project will provide key information regarding the prevalence rates of cravings, anxiety, and depressive symptoms among persons with alcohol and substance use disorders; demographic correlates of cravings, anxiety, and depression; and outcome data related to this scalable population-level intervention. Information from this study will be valuable for addiction care practitioners; it will inform the policy and decision making regarding population-level addiction treatment and support during emergencies. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/22047
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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 | 0.032 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.069 | 0.014 |
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".