The Integration of the Program Evaluation Standards into an Evaluation Toolkit for a Transformative Model of Care for Mental Health Service Delivery
Bibliographic record
Abstract
Background: Stepped Care 2.0 (SC2.0) is a transformative model of mental health service delivery. This model was created by Stepped Care Solutions (SCS), a not-for-profit consultancy that collaborates with governments, public service organizations, and other institutions that wish to redesign their mental health and addictions systems of care. The SC2.0 model is based on 10 foundational principles and 9 core components that can be flexibly adapted to an organization’s or community’s needs. The model supports groups to reorganize and deliver mental health care in an evidence-informed, person-centric way. SCS partnered with evaluators from the Centre for Health Evaluation and Outcome Sciences (CHÉOS) to create a toolkit that provides evaluation guidance. The toolkit includes a theory of change, guidance on selecting evaluation questions and designs, and an evaluation matrix including suggested process and outcome metrics, all of which can be tailored to each unique implementation of the SC2.0 model. The objective of this resource is to support organizations and communities to conduct high-quality evaluations for the purpose of continuous improvement (a core component of the model of care) and to assess the model’s impact. Purpose: The purpose of this paper is to discuss the integration of the program evaluation standards (PES) into an evaluation toolkit for SC2.0. Setting: In this paper, we describe the toolkit development, focusing on how the PES were embedded in the process and tools. We explore how the integration of the PES into the toolkit supports evaluators to enhance the quality of their evaluation planning, execution, and meta-evaluation. Intervention: Not applicable Research Design: Not applicable Data Collection and Analysis: Not applicable Findings: In this paper, we describe the toolkit development, focusing on how the PES were embedded in the process and tools. We explore how the integration of the PES into the toolkit supports evaluators to enhance the quality of their evaluation planning, execution, and meta-evaluation.
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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 | 0.503 | 0.504 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".