The Experience of Patients Engaged in Co-designing Care Processes
Bibliographic record
Abstract
This article presents the experiences of patients engaged in co-designing care under a program entitled, "Transforming Care at the Bedside," based at an academic health sciences center. This descriptive, qualitative study collected data through individual interviews. Participants included patients from 5 units in an academic health sciences center in Quebec, Canada. A total of 6 individual interviews were conducted in November 2014, 15 months after the Transforming Care at the Bedside work began in September 2013. Content analysis was used to analyze the qualitative data. Being listened to and informed gave patients an opportunity to better understand patient needs and the complexity of care in the unit and in the organization. The experience enabled patients to better translate the patient experience for the team's benefit and influence the team's perspective and decisions. Through this experience, several patients felt motivated and empowered and that they afforded consideration through this experience. This study highlights the importance of creating opportunities for patients and health care providers to share their unique experiences and expertise to better understand each other's reality. In this context, they developed a more comprehensive understanding of the issues and worked together to implement realistic changes on behalf of the patients.
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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 | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".