Evaluating the impact of engagement: An introduction to the Engage with Impact Toolkit
Bibliographic record
Abstract
Introduction: Patient and public engagement and involvement has become embedded in many sectors of the health system internationally, from research to health system governance. There is growing interest in understanding how to evaluate the impact of this engagement on individuals involved, organizations, programs and on health outcomes but few tools are available to support this work. To meet this need, we developed the Engage with Impact Toolkit, an evaluation resource to support health system organizations and researchers to evaluate the impacts of patient engagement. Methods And Involvement: The toolkit was co-designed with the Evaluating Patient Engagement Working Group, a multidisciplinary group of patient partners, engagement staff, researchers and government personnel working within the health sector in Ontario, Canada. The working group members fully collaborated to co-develop the evaluation toolkit. Work began with a project scoping phase and a literature review. Working group members identified their priorities for this work, and the literature was reviewed to identify previously noted impacts of engagement. A modified concept mapping approach followed. A brainstorming survey was sent to the working group and a broader group of patient partners and engagement leads to identify potential impacts of engagement on individuals, programs, organizations and health systems. The brainstormed items were sorted and combined into a smaller set of items. A second survey was conducted to rate the importance of evaluating each item. In the final stage, the working group deliberated to finalize the items and design the toolkit. Results: The scoping phase resulted in a co-developed goal of creating an evaluation toolkit that was practical, accessible and menu-driven. Concept mapping process with members of the engagement community in Ontario led to 237 impact statements that were sorted, discussed and combined into 81 unique items. Following further rating and sorting, the items were consolidated into a final list of 35 impacts which we mapped across 8 domains: (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. The working group worked to create a web-based toolkit that includes these impact items along with evaluation guidance, which is hosted at www.evaluateengagement.ca Impacts, Learnings And Next Steps: The online toolkit is now publicly available in French and English. The toolkit has been used to evaluate several patient engagement approaches within the health system, government and research teams to date. While the toolkit was developed with the Ontario context in mind, it has been used by groups in several Canadian jurisdictions and we anticipate that it will be useful in international settings. The learnings from this approach could be used by groups in other regions to refine the toolkit and impact items for their own contexts, in partnership with patient partners and other stakeholders. Further work is underway to evaluate the toolkit itself with end users. The toolkit will continue to be updated and modified based on user feedback.
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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 | 0.091 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".