From Craft to Reflective Art and Science Comment on "Metrics and Evaluation Tools for Patient Engagement in Healthcare Organization- and System-Level Decision-Making: A Systematic Review"
Bibliographic record
Abstract
Patient engagement practices are increasingly incorporated in health research, governance, and care. More recently, a large number of evaluation tools and metrics have been developed to support engagement evaluation. This growing interest in evaluation reflects a maturation of the patient engagement field, moving from a "craft" to a reflective "art and science," with more explicit expected benefits and risks, better understood conditions for success and failure, and increasingly rigorous evaluation instruments to improve engagement theories and interventions. It also supports a more critical view of engagement science, moving beyond reductionist views of engagement as a "black box technology" to a more subtle view of this broad category of complex interventions. Structured evaluation can advance patient engagement by supporting more reflective partnerships between patients, clinicians, health system leaders and citizens. This can help clarify mutual (and potentially contradictory) expectations toward engagement, provide a reality check toward claims of benefits and harms, and increase health systems' capacity to implement effective engagement practices over time. To do so, closer collaborations are required between engagement scientists and practitioners to align the theories, practice and evaluation of patient and community engagement.
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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 | 0.050 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.046 | 0.047 |
| Insufficient payload (model declined to judge) | 0.006 | 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".