Challenges of Collaborative Improvement in Complex Continuing Care
Bibliographic record
Abstract
The Improving Continence Care in Complex Continuing Care (IC 5) Collaborative Project was the first multi-hospital quality improvement project conducted by the Hospital Report Research Collaborative aimed at the complex continuing care sector. Using the Breakthrough Series collaborative methodology developed by the Institute for Healthcare Improvement, 12 teams from various hospitals across Ontario worked together for 10 months under the guidance of quality improvement consultants and content experts in continence care. The project was carried out by the University of Toronto IC 5 research team, composed of principal investigators, clinical and improvement experts, coaches and a project manager. This article shares what it was like to participate in the IC 5 Collaborative Project. It presents interviews conducted with a sample of the participants and the coaches to gain a richer understanding of their IC 5 experience. The article discusses key points for organizations to consider before they engage in an improvement collaborative, based on the participants' views of what worked well and challenges they experienced, and suggestions about what they would do differently.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.235 | 0.284 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.035 | 0.040 |
| Scholarly communication | 0.039 | 0.030 |
| Open science | 0.010 | 0.038 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".