Qualitative Evaluation of a Quality Improvement Collaborative Implementation to Improve Acute Ischemic Stroke Treatment in Nova Scotia, Canada
Bibliographic record
Abstract
The Atlantic Canada Together Enhancing Acute Stroke Treatment (ACTEAST) project is a modified quality improvement collaborative (mQIC) designed to improve ischemic stroke treatment rates and efficiency in Atlantic Canada. This study evaluated the implementation of the mQIC in Nova Scotia using qualitative methods. The mQIC spanned 6 months, including two learning sessions, webinars, and a per-site virtual visit. The learning sessions featured presentations about the project and the improvement efforts at some sites. Each session included an action planning period where the participants planned for the implementation efforts over the following 2 to 4 months, called "action periods". Eleven hospitals and Emergency Health Services (EHS) of Nova Scotia participated. The Consolidated Framework for Implementation Research (CFIR) was utilized to develop a semi-structured interview guide to uncover barriers and facilitators to mQIC's implementation. Interviews were conducted with 14 healthcare professionals from 10 entities, generating 458 references coded into 28 CFIR constructs. The interviews started on 17 June 2021, 2 months after the intervention period, and ended on 7 October 2021. Notably, 84% of these references were positively framed as facilitators., highlighting the various aspects of the mQIC and its context that supported successful implementation. These facilitators encompassed factors such as networks and communications, strong leadership engagement, and a collaborative culture. Significant barriers included resource availability, relative priorities, communication challenges, and engaging key stakeholders. Some barriers were prominent during specific phases. The study provides insights into quality improvement initiatives in stroke care, reflecting the generally positive opinions of the interviewees regarding the mQIC. While the quantitative analysis is still ongoing, this study highlights the importance of addressing context-specific barriers and leveraging the identified facilitators for successful implementation.
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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 | 0.023 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".