Evaluating the Complex: Alternative Models and Measures for Evaluating Collaboration among Substance use Services with Mental Health, Primary Care and other Services and Sectors
Bibliographic record
Abstract
Many planners and administrators now look to “collaboration” or “integration” as a solution, or at least a partial solution, to challenges related to access and delivery of substance use and mental health services and health services in general. Among the major constraints in identifying best practices in this area and recommending optimal evaluation strategies are the plethora of terms and concepts used in the literature to describe collaboration or integration as well as the many alternative approaches and outcome expectations. It is helpful, therefore, to follow concrete steps to plan the evaluation, including the engagement of multiple stakeholders in the planning process and subsequent execution of the evaluation. The concrete evaluation strategies employed can follow a traditional, often linear model, of impact and are often categorized under the common typologies of process, outcome or economic evaluations. Each approach examines different domains of interest and can be at the individual/service level or at the level of the overall treatment system. Other less traditional evaluation models and methods based on systems theory, complex adaptive systems and developmental evaluation have much to offer the evaluation of interventions aimed at improving the collaboration and integration of substance use services with other health and social services and sectors. Realist evaluation is a particularly helpful approach that integrates many of the traditional approaches with these other models and methods.
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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 | 0.141 | 0.244 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| 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".