Patient partnership in quality improvement of healthcare services: Patients’ inputs and challenges faced
Bibliographic record
Abstract
This research focuses on the perception of patients who participated in Continuous Quality Improvement Committees (CIC) regarding their contribution, lessons learned, and challenges encountered. The committees are engaged in a care partnership approach where patients are recognized for their experiential knowledge and treated as full members of the clinical team. Based on patient interviews, we conclude that they bring a structured and thoughtful vision of their experience. They identify themselves as real partners in the care process and are grateful for the opportunity to improve the care provided to other patients by using their own experience and by bringing changes to the patient-professional relationship, particularly in terms of communication. They also become better acquainted with the complexity of the health system and its organization. However, their participation in CICs raised two challenges. The first was their availability, as their professional schedules did not always allow them to participate in meetings. The second was their frustration with the slow decision-making process and implementation of necessary measures for quality improvement of healthcare and services. This study highlights the contribution of successful patient participation to quality of care improvement. Experience Framework This article is associated with the Patient, Family & Community Engagement lens of The Beryl Institute Experience Framework. (http://bit.ly/ExperienceFramework) Access other PXJ articles related to this lens. Access other resources related to this lens.
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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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| 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".