Inside the Black Box: Challenges in Implementation Evaluation of Community Mental Health Case Management Programs
Bibliographic record
Abstract
Abstract: Fidelity measurement is an evolving field in mental health case management program evaluation. This article presents an exploratory study in which two separate fidelity measures, the Dartmouth Assertive Community Treatment Scale (DACTS) and the Key Component Profiles (KCP), were used to assess structure and process elements of three mental health case management programs. The programs were studied because they all provided services to seriously mentally ill inner city populations and shared a common context for practice. However, one program followed the Assertive Community Treatment (ACT) model, the other two were Intensive Case Management (ICM) programs, and one of the ICM programs formed a significant partnership with a home care agency for service delivery. The extent to which the DACTS and KCP were able to measure the structure and process similarities and differences of the programs is examined. The results provide information for evaluators on the possible strengths and limitations of each fidelity tool in differentiating various elements of the case management models and reinforce the importance of assessing program fidelity from a multi-dimensional perspective.
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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 | 0.715 | 0.765 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".