Considering a multisite study? How to take the leap and have a soft landing
Bibliographic record
Abstract
Abstract Although most policymakers agree that a fundamental goal of the mental health system is to provide integrated community‐based services, there is little empirical evidence with which to plan such a system. Studies in the community mental health literature have not used a standard set of evaluation methods. One way of addressing this gap is through a multisite program evaluation in which multiple sites and programs evaluate the same outcomes using the same instruments and time frame. The proposition of introducing the same study design in different settings and programs is deceptively straightforward. The difficulty is not in the conceptualization but in the implementation. This article examines the factors that act as implementation barriers, how are they magnified in a multisite study design, and how they can be successfully addressed. In discussing the issue of study design, this article considers processes used to address six major types of barriers to conducting collaborative studies identified by Lancaster or Lancaster's six Cs—contribution, communication, compatibility, consensus, credit, and commitment. A case study approach is used to examine implementation of a multisite community mental health evaluation of services and supports (case management, self‐help initiatives, crisis interventions) represented by six independent evaluations of 15 community health programs. A principal finding was that one of the main vehicles to a successful multisite project is participation. It is only through participation that Lancaster's six Cs can be addressed. Key factors in large, geographically dispersed, and diverse groups include the use of advisory committees, explicit criteria and opportunities for participation, reliance on all modes of communication, and valuing informal interactions. The article concludes that whereas modern technology has assisted in making complicated research designs feasible, the operationalization of timeless virtues such as mutual respect and trust, flexibility, and commitment make them successful. © 2002 John Wiley & Sons, Inc.
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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 | 0.643 | 0.735 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.032 | 0.047 |
| Scholarly communication | 0.048 | 0.065 |
| Open science | 0.021 | 0.046 |
| Research integrity | 0.053 | 0.057 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".