Supporting the evaluation of public and patient engagement in health system organizations: Results from an implementation research study
Bibliographic record
Abstract
BACKGROUND: As citizens, patients and family members are participating in numerous and expanding roles in health system organizations, attention has turned to evaluating these efforts. The context-specific nature of engagement requires evaluation tools to be carefully designed for optimal use. We sought to address this need by assessing the appropriateness and feasibility of a generic tool across a range of health system organizations, engagement activities and patient groups. METHODS: We used a mixed-methods implementation research design to study the implementation of an engagement evaluation tool in seven health system organizations in Ontario, Canada focusing on two key implementation outcome variables: appropriateness and feasibility. Data were collected through respondent feedback questions (binary and open-ended) at the end of the tool's three questionnaires as well as interviews and debriefing discussions with engagement professionals and patient partners from collaborating organizations. RESULTS: The three questionnaires comprising the evaluation tool were collectively administered 29 times to 405 respondents yielding a 52% response rate (90% and 53% of respondents respectively assessed the survey's appropriateness and feasibility [quantitatively or qualitatively]). The questionnaires' basic properties were rated highly by all respondents. Concrete suggestions were provided for improving the appropriateness and feasibility of the questionnaires (or components within) for different engagement activity and organization types, and for enhancing the timing of implementation. DISCUSSION AND CONCLUSIONS: Our study findings offer guidance for health system organizations and evaluators to support the optimal use of engagement evaluation tools across a variety of health system settings, engagement activities and respondent groups.
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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 | 0.275 | 0.297 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".