Improving care for advanced COPD through practice change: Experiences of participation in a Canadian spread collaborative
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) is a leading cause of death, morbidity, and health-care spending. The Halifax, Nova Scotia-based INSPIRED COPD Outreach Program™ has proved highly beneficial for patients and the health-care system. With direct investment of <$1-million CAD, a pan-Canadian quality improvement collaborative (QIC) supported the spread of INSPIRED to 19 teams in the 10 Canadian provinces contingent upon participation in evaluation. The collaborative evaluation followed a mixed-methods summative approach relying on collated quantitative data, team documents, and surveys sent to core members of the 19 teams. Survey questions included a series of multiple-choice responses, Likert scale ratings, and open-ended questions. The qualitative evaluation entailed key informant interviews and focus groups undertaken between February and April 2016 post-collaborative. Teams reported that the year-long QIC helped bring focus to a needed, though often overlooked area of improvement, facilitating innovation spread. They report examples of new work practices as well as unanticipated cultural change (given the short QIC time frame). Most teams gained new skills in quality improvement (QI) and evidence-based medicine, showing progress in their ability to measure and implement COPD care improvements. Teams felt networking with other teams across the country toward a common solution as well as learning from a team of clinical innovators and evidence-based innovation were critical to their success. Factors affecting sustainability included local leadership support, involvement of frontline clinicians, and sharing milestones to motivate continued QI. The INSPIRED QIC enabled teams across Canada to adapt and implement a new COPD care model for high users of health-care with rapid improvements to work practices, cultural change, and skill sets, and at relatively low cost.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".