Envisioning mechanisms for success: Evaluation of EBCD at CHEO
Bibliographic record
Abstract
To advance patient engagement (PE) and more comprehensively involve patients, families, and staff in quality improvement (QI) at the Children’s Hospital of Eastern Ontario (CHEO), the Experience Based Co-Design (EBCD) approach was piloted. Set against the backdrop of envisioning factors that would facilitate success, an evaluation was designed to assess five domains: strengthening of mutual understanding, collaboration, and partnerships between patients/families and staff; a greater involvement of patients, families, and staff in QI; satisfaction with the process; the ability of EBCD to generate clear and useful data to ascertain the patient/family and staff experience; and the ability of EBCD to generate clear and useful data to improve patient/family and staff experience. The King’s Fund EBCD toolkit was followed to implement the approach. This involved observations and interviews to capture experiences; and feedback events to understand experiences and identify improvement areas. The resulting data was used to evaluate the process relative to the five domains of interest. The evaluation data supported the conclusion that the EBCD process usefully addressed each of the domains of interest, and facilitated PE in QI. In addition, the evaluation revealed important considerations to the success of such an initiative. Using EBCD allows for a more nuanced and comprehensive consultation than traditional methods employed. The research presented here informs the future spread and/or adaptation of EBCD by offering a case for using the approach, but also suggests modifications or considerations to integrate PE methods with existing structures for greater efficiency, success, and value.
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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 | 0.154 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 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".