Development of the Engage with Impact Toolkit: A comprehensive resource to support the evaluation of patient, family and caregiver engagement in health systems
Bibliographic record
Abstract
INTRODUCTION: Recent shifts in the patient, family and caregiver engagement field have focused greater attention on measurement and evaluation, including the impacts of engagement efforts. Current evaluation tools offer limited support to organizations seeking to reorient their efforts in this way. We addressed this gap through the development of an impact measurement framework and accompanying evaluation toolkit-the Engage with Impact Toolkit. METHODS: The measurement framework and toolkit were co-designed with the Evaluating Patient Engagement Working Group, a multidisciplinary group of patient, family and caregiver partners, engagement specialists, researchers and government personnel. Project activities occurred over four phases: (1) project scoping and literature review; (2) modified concept mapping; (3) working group deliberations and (4) toolkit web design. RESULTS: The project scope was to develop a measurement framework and an evaluation toolkit for patient engagement in health systems that were practical, accessible, menu-driven and aligned with current system priorities. Concept mapping yielded 237 impact statements that were sorted, discussed and combined into 81 unique items. A shorter list of 50 items (rated 8.0 or higher out of 10) was further consolidated to generate a final list of 35 items mapped across 8 conceptual domains of impact: (1) knowledge and skills; (2) confidence and trust; (3) equity and inclusivity; (4) priorities and decisions; (5) effectiveness and efficiency; (6) patient-centredness; (7) culture change and (8) patient outcomes and experience. Working Group members rated the final list for importance (1-5) and identified a core set of 33 items (one for each of the 8 domains and 25 supplementary items). Two domains (priorities and decisions; and culture change) yielded the highest overall importance ratings (4.8). A web-based toolkit (www.evaluateengagement.ca) hosts the measurement framework and related evaluation supports. CONCLUSION: The Engage with Impact Toolkit builds on existing engagement evaluation tools but brings a more explicit focus to supporting organizations to assess the impacts of their engagement work. PATIENT CONTRIBUTION: Patient, family and caregiver partners led the early conceptualization of this work and were involved at all stages and in all aspects of the work. As end-users of the toolkit, their perspectives, knowledge and opinions were critical.
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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 | 0.105 | 0.181 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".