Measurement-Based Care in Treatment of Substance Use Disorders
Bibliographic record
Abstract
Introduction Measurement-Based Care (MBC) is an emerging healthcare model with a number of potential advantages over traditional approaches for the treatment of substance use disorder (SUD). Despite SUD treatment programs being theoretically well suited for the implementation of MBC, its uptake has been minimal, which in turn limits further research, knowledge synthesis, and translation into clinical practice. Objectives The goal of this knowledge synthesis project is to stimulate greater consideration of MBC models in addictions programs, with three interrelated objectives: 1. To summarize the existing evidence from research literature 2. To complement the literature findings with the data from our clinical research and quality improvement projects 3. To explore potential risks and difficulties of MBC implementation in the SUD treatment programs Methods Narrative review. Knowledge synthesis. Results To date, only two published randomized controlled trials, which along with the data from our pragmatic clinical research, support the wider implementation of MBC in the substance abuse treatment settings, but also indicate the high need for larger-scale clinical trials and quality improvement programs. Potential barriers to the implementation of MBC for SUD are outlined at the patient, provider, organization, and system levels, as well as challenges associated with the use of MBC programs for clinical research. Critical thinking considerations and risk mitigation strategies are offered toward advancing MBC for SUD beyond the current nascent state. Conclusions The state-of-the-art of MBC in SUD care settings reviewed and the strategies for further development from adminsitrative, clinical, and research prospectives outlined. Disclosure No significant relationships.
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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 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".