Patient engagement in the nonclinical setting: A concept analysis
Bibliographic record
Abstract
OBJECTIVE: Redesigning of health care through patient engagement at policy levels has been declared as the 21st-century solution to improving health outcomes of patients, enhancing patient safety, and reducing climbing health care costs. Despite these optimistic claims, conceptual clarity regarding patient engagement is lacking, thereby limiting the potential for both taking up this engagement and evaluating its effectiveness. Of particular interest is patient engagement in nonclinical settings, meaning engagement at more strategic tables. METHODOLOGY: A conceptual analysis, of patient engagement within nonclinical settings, using Walker and Avant's eight-step method. RESULTS: Four key attributes are identified for patient engagement within the nonclinical setting: power, communication, collaboration, and information sharing. Patient engagement is defined as a process in which patients, caregivers, and health care professionals collaborate as equal partners, contributing unique skills while sharing information and perspectives toward innovative ideas that contribute to the overall improvement of health care. CONCLUSION: The concept of patient engagement carries with it, a long journey of milestones and learning, yet continues to lack clarity. Obtaining conceptual clarity is a necessary step to developing reliable methods of measuring the actual contribution of patient engagement in health care system improvements.
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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 | 0.033 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".