Real‐World Implementation of Contingency Management and Benefits of a Controlled Environment “Head Start”
Bibliographic record
Abstract
OBJECTIVES: To use quality improvement (QI) principles to implement contingency management (CM) in an outpatient clinic. METHODS: Prize-based CM was implemented for stimulant use disorder using standard protocols with QI processes used to improve efficiency and effectiveness. RESULTS: CM was successfully implemented in an outpatient addictions clinic using clinical funds. Participants who were discharged to the outpatient clinic from a controlled environment (such as hospital, withdrawal management, residential treatment program) had 8.3 (SD = 3.1) weeks of consecutive abstinence (CA) and 10.7 (SD = 1.0) weeks total abstinence. CONCLUSIONS: Pragmatic clinical implementation of CM yielded results that are comparable to controlled trials in similar populations. QI processes identified that a controlled environment resulted in significant ongoing abstinence.CM was implemented in a small community clinic with no additional funding, and QI principles were used to improve efficiency and effectiveness. The initial implementation of 17 participants yielded a mean of 2.2 (SD = 3.2) weeks of CA. Recent discharge from a controlled environment resulted in 8.3 (3.1) weeks CA.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".