The evaluation of treatment services and systems for substance use disorders
Bibliographic record
Abstract
Scientific research and program evaluation have not played a major role in shaping the development of treatment services and systems in most countries. This has led to disparities in the development, management and monitoring of national treatment systems. In the evaluation of treatment for substance use disorders, the evaluation practitioner will usually be working at one of five levels: single case, treatment activity, treatment service, treatment agency or treatment system. One of the major barriers to undertaking internal program evaluation is the belief that it is a complicated research process best left to those with specific research training. Program managers and staff can plan and initiate an evaluation process for their program if they have access to research expertise when needed for certain parts of the process. There are seven main components of an evaluation process that can be planned and implemented: need assessment; evaluation planning, process evaluation, cost analysis, client satisfaction evaluation, outcome evaluation and economic evaluation. However, evaluation is more than the techniques and technology required to implement these types of activities. It also involves the routine questioning of current practice even if the feedback may be less positive than anticipated. A healthy culture for evaluation is one in which feedback loops are woven into the fabric of the treatment service or system. There are many barriers to evaluation in substance abuse services but these barriers can be overcome with careful planning and commitment to the delivery of evidence-based services.
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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 | 0.092 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".