Supporting the implementation of stroke quality-based procedures (QBPs): a mixed methods evaluation to identify knowledge translation activities, knowledge translation interventions, and determinants of implementation across Ontario
Bibliographic record
Abstract
BACKGROUND: In 2013, Health Quality Ontario introduced stroke quality-based procedures (QBPs) to promote use of evidence-based practices for patients with stroke in Ontario hospitals. The study purpose was to: (a) describe the knowledge translation (KT) interventions used to support stroke QBP implementation, (b) assess differences in the planned and reported KT interventions by region, and (c) explore determinants perceived to have affected outcomes. METHODS: A mixed methods approach was used to evaluate: activities, KT interventions, and determinants of stroke QBP implementation. In Phase 1, a document review of regional stroke network work plans was conducted to capture the types of KT activities planned at a regional level; these were mapped to the knowledge to action framework. In Phase 2, we surveyed Ontario hospital staff to identify the KT interventions used to support QBP implementation at an organizational level. Phase 3 involved qualitative interviews with staff to elucidate deeper understanding of survey findings. RESULTS: Of the 446 activities identified in the document review, the most common were 'dissemination' (24.2%; n = 108), 'implementation' (22.6%; n = 101), 'implementation planning' (15.0%; n = 67), and 'knowledge tools' (10.5%; n = 47). Based on survey data (n = 489), commonly reported KT interventions included: staff educational meetings (43.1%; n = 154), champions (41.5%; n = 148), and staff educational materials (40.6%; n = 145). Survey participants perceived stroke QBP implementation to be successful (median = 5/7; interquartile range = 4-6; range = 1-7; n = 335). Forty-four people (e.g., managers, senior leaders, regional stroke network representatives, and frontline staff) participated in interviews/focus groups. Perceived facilitators to QBP implementation included networks and collaborations with external organizations, leadership engagement, and hospital prioritization of stroke QBP. Perceived barriers included lack of funding, size of the hospital (i.e., too small), lack of resources (i.e., staff and time), and simultaneous implementation of other QBPs. CONCLUSIONS: Information on the types of activities and KT interventions used to support stroke QBP implementation and the key determinants influencing uptake of stroke QBPs can be used to inform future activities including the development and evaluation of interventions to address barriers and leverage facilitators.
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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 | 0.133 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".