The Quality in Acute Stroke Care (QASC) global scale-up using a cascading facilitation framework: a qualitative process evaluation
Bibliographic record
Abstract
BACKGROUND: Variation in hospital stroke care is problematic. The Quality in Acute Stroke (QASC) Australia trial demonstrated reductions in death and disability through supported implementation of nurse-led, evidence-based protocols to manage fever, hyperglycaemia (sugar) and swallowing (FeSS Protocols) following stroke. Subsequently, a pre-test/post-test study was conducted in acute stroke wards in 64 hospitals in 17 European countries to evaluate upscale of the FeSS Protocols. Implementation across countries was underpinned by a cascading facilitation framework of multi-stakeholder support involving academic partners and a not-for-profit health organisation, the Angels Initiative (the industry partner), that operates to promote evidence-based treatments in stroke centres. .We report here an a priori qualitative process evaluation undertaken to identify factors that influenced international implementation of the FeSS Protocols using a cascading facilitation framework. METHODS: The sampling frame for interviews was: (1) Executives/Steering Committee members, consisting of academics, the Angels Initiative and senior project team, (2) Angel Team leaders (managers of Angel Consultants), (3) Angel Consultants (responsible for assisting facilitation of FeSS Protocols into multiple hospitals) and (4) Country Co-ordinators (senior stroke nurses with country and hospital-level responsibilities for facilitating the introduction of the FeSS Protocols). A semi-structured interview elicited participant views on the factorsthat influenced engagement of stakeholders with the project and preparation for and implementation of the FeSS Protocol upscale. Interviews were recorded, transcribed verbatim and analysed inductively within NVivo. RESULTS: Individual (n = 13) and three group interviews (3 participants in each group) were undertaken. Three main themes with sub-themes were identified that represented key factors influencing upscale: (1) readiness for change (sub-themes: negotiating expectations; intervention feasible and acceptable; shared goal of evidence-based stroke management); (2) roles and relationships (sub-themes: defining and establishing roles; harnessing nurse champions) and (3) managing multiple changes (sub-themes: accommodating and responding to variation; more than clinical change; multi-layered communication framework). CONCLUSION: A cascading facilitation model involving a partnership between evidence producers (academic partners), knowledge brokers (industry partner, Angels Initiative) and evidence adopters (stroke clinicians) overcame multiple challenges involved in international evidence translation. Capacity to manage, negotiate and adapt to multi-level changes and strategic engagement of different stakeholders supported adoption of nurse-initiated stroke protocols within Europe. This model has promise for other large-scale evidence translation programs.
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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 | 0.149 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".