In Pursuit of Better Care Transitions: Lessons Learned from a Co-Designed Project
Bibliographic record
Abstract
In this commentary, we reflect on our experience of co-designing an intervention to address challenges due to delayed hospital discharge (known as alternate level of care in Canada).Through a series of focus groups and co-design sessions, we identified common challenges with delayed discharge (including a lack of services while waiting for discharge and poor communication with the care team).In co-designing service improvements, we (1) amplified the voices of patients and caregivers, which helped them feel unified in their experience and (2) developed tools that aim to improve patient, caregiver and provider experiences.In this commentary, we reflect on these impacts along with the key lessons learned.P = Patient or caregiver partner. Key Points• We co-created a strategy (i.e., components of an intervention) to address challenges with delayed hospital discharge (a care quality issue experienced by health systems worldwide).• Starting at the very beginning of the project, shared leadership (i.e., shared power) among all stakeholders was essential to create a safe space to open up.• Projects can lose momentum if participants do not stay connected to people (i.e., decision makers) who have the power to make the change required for the co-designed activity/intervention to be adopted and implemented.
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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 | 0.142 | 0.247 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.015 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.011 | 0.018 |
| 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".