Integrating Engagement and Improvement Work in a Pediatric Hospital
Bibliographic record
Abstract
Although geared towards a common goal - improved patient-centred care - quality improvement strategies and patient engagement-focused approaches are often developed and conducted in silos. The lack of integration may lead, on the one hand, to the uptake of patient suggestions that do not always take into consideration implications for the delivery of quality care and, on the other hand, to inadequate understanding of patient views required to create optimal services. The Children's Hospital of Eastern Ontario (CHEO)'s action plans to address gaps in patient engagement and quality improvement, two of its priority areas, were initially carried out in isolation of each other. While implementing a key patient engagement initiative using an experience-based co-design approach, Lean process improvement tools were used to plan and implement projects to improve patient, family and staff experiences of care. Preliminary assessments of this project revealed that the integration of these two approaches is feasible and that it was well received by both staff and families. There is important synergy to be found between patient engagement and quality improvement that needs to be leveraged by organizational structures and processes to fulfill the commitments inherent in both fields.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| 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".