Patient engagement in care: A scoping review of recently validated tools assessing patients' and healthcare professionals' preferences and experience
Bibliographic record
Abstract
BACKGROUND: Patient engagement in care is a priority and a key component of clinical practice. Different approaches to care have been introduced to foster patient engagement. There is a lack of a recent review on tools for assessing the main concepts and dimensions related to patient engagement in care. OBJECTIVE: Our scoping review sought to map and summarize recently validated tools for assessing various concepts and dimensions of patient engagement in care. SEARCH STRATEGY: A scoping review of recent peer-reviewed articles describing tools that assess preferences in and experience with patient engagement in care was conducted in four databases (Ovid Medline, Ovid EMBASE, Cochrane Database of Systematic Reviews, CINAHL-EBSCO). We adopted a broad definition based on the main concepts of patient engagement in care: patient-centredness, empowerment, shared decision-making and partnership in care. MAIN RESULTS: Of 2161 articles found, 16, each describing a different tool, were included and analysed. Shared decision-making and patient-centredness are the two main concepts evaluated, often simultaneously in most of the tools. Only four scales measure patient-centredness, empowerment and shared decision-making at the same time, but no tool measures the core dimensions of partnership in care. Most of the tools did not include patients in their development or validation or just consulted them during the validation phase. DISCUSSION AND CONCLUSION: There is no tool coconstructed with patients from development to validation, which can be used to assess the main concepts and dimensions of patient engagement in care at the same time. PATIENT AND PUBLIC CONTRIBUTION: This manuscript was prepared with a patient expert who is one of the authors. Vincent Dumez, who is a patient expert and codirector of the Center of Excellence on Partnership with Patients and the Public, has contributed to the preparation of the manuscript.
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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 | 0.059 | 0.186 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.032 | 0.030 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".