Hospital organizational context and delivery of evidence-based stroke care: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Organizational context is one factor influencing the translation of evidence into practice, but data pertaining to patients with acute stroke are limited. We aimed to determine the associations of organizational context in relation to four important evidence-based stroke care processes. METHODS: This was a mixed methods cross-sectional study. Among 19 hospitals in Queensland, Australia, a survey was conducted of the perceptions of stroke clinicians about their work using the Alberta Context Tool (ACT), a validated measure covering 10 concepts of organizational context, and with additional stroke-specific contextual questions. These data were linked to the Australian Stroke Clinical Registry (AuSCR) to determine the relationship with receipt of evidence-based acute stroke care (acute stroke unit admission, use of thrombolysis for those with acute ischemic stroke, receipt of a written care plan on discharge, and prescription of antihypertensive medications on discharge) using quantile regression. Exploratory cluster analysis was used to categorize hospitals into high and low context groups based on all of the 10 ACT concepts. Differences in adherence to care processes between the two groups were examined. RESULTS: A total of 215 clinicians completed the survey (50% nurses, 37% allied health staff, 10% medical practitioners), with 81% being in their current role for at least 1 year. There was good reliability (∞ 0.83) within the cohort to allow pooling of professional groups. Greater ACT scores, especially for social capital (μ 9.00, 95% confidence interval [CI] 4.86 to 13.14) and culture (μ 7.33, 95% CI 2.05 to 12.62), were associated with more patients receiving stroke unit care. There was no correlation between ACT concepts and other care processes. Working within higher compared to lower context environments was associated with greater proportions of patients receiving stroke unit care (88.5% vs. 69.0%) and being prescribed antihypertensive medication at discharge (62.5% vs. 52.0%). Staff from higher context hospitals were more likely to value medical and/or nursing leadership and stroke care protocols. CONCLUSIONS: Overall organizational context, and in particular aspects of culture and social capital, are associated with the delivery of some components of evidence-based stroke care, offering insights into potential pathways for improving the implementation of proven therapies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".