Development and testing of an interactive evaluation tool: the Evaluating QUality and ImPlementation (EQUIP) Tool
Bibliographic record
Abstract
BACKGROUND: Evaluating implementation outcomes is gaining momentum in health service delivery organizations. Teams are increasingly recognizing the importance of capturing and learning from their implementation efforts, and Implementation Scientists have published extensively on implementation outcomes. However, Quality Improvement approaches and tools are more widely recognized and routinely used in healthcare to improve processes and outcomes. This article describes the development of an interactive online tool designed to help researchers and practitioners effectively design and develop appropriate evaluation plans that support the understanding of successful implementation. METHODS: There were two main development phases. Phase 1, from January to October 2020, involved several design sessions with a small group of professionals leading implementation initiatives within the provincial health delivery system. This resulted in a testable prototype. Phase 2, from November 2020 to June 2021, focused on usability testing and interviews with a broader group of researchers and professionals leading implementation initiatives across the province. RESULTS: The result is the EQUIP (Evaluating QUality and ImPlementation) Tool, an interactive online tool that integrates quality measures from the Alberta Quality Matrix for Health and implementation measures from widely used outcomes frameworks, such as the one developed by Proctor and colleagues and the RE-AIM planning and evaluation framework. The tool encourages users to explore implementation outcomes and quality dimensions from different perspectives and select questions and indicators relevant to their project. CONCLUSION: The EQUIP tool was designed and refined in collaboration with end users to create an accessible and practical online tool. This work addresses the call for greater integration of Quality Improvement and Implementation Science by combining approaches from both fields to strengthen evaluation processes within the health system.
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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 | 0.113 | 0.266 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".