Boosting Drug Treatment Attendance Through Police-Sent Text Message Nudges: A Randomized Controlled Trial with Drug-Positive Arrestees
Bibliographic record
Abstract
Abstract Attrition from drug treatment programs is a ubiquitous concern, but less is known about effective strategies to assist people with an addiction in arriving at the initial intake meeting. This study investigates whether text message reminders sent to drug-positive arrestees to participate in mandated drug treatment appointments increase attendance rates. We conducted a randomized controlled trial in London, and participants were randomly assigned to either a treatment group (n = 403) receiving a text message reminder or a control group (n = 410) receiving no text message. Participants were arrestees with a verified mobile phone number who tested positive for Class A drugs at intake across 25 custody suites and were scheduled for a drug treatment assessment at one of London’s 28 treatment facilities. The primary outcome was the attendance rate at drug treatment centers, which was analyzed using an ordinary least squares regression model. Results suggest that nudges have the potential to increase attendance at drug treatment centers among drug-positive arrestees. Although we have no additional outcome variables, the intervention shows promise as a cost-effective strategy for enhancing compliance with mandated rehabilitations. Future research should explore this intervention’s broader implications and effectiveness across diverse and more extensive samples.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".