Improving stroke nursing orientation through a stroke learning resource
Bibliographic record
Abstract
Background: Canadian Stroke Best Practice recommends that patients suffering from an acute stroke be treated on an interprofessional stroke unit with recommended levels of medical, nursing, physiotherapy, occupational therapy, speech language pathology, social work, and dieticians. Potential gaps in knowledge and complexity of required care highlight the need for more nursing education. There is currently a lack of stroke specific resources available for nurses during orientation. Purpose: To develop a stroke learning resource which highlights Canadian Stroke Best Practice Recommendations, interprofessional roles and responsibilities, and the essential nursing role on an acute stroke unit. Methods: The learning resource was developed based on information obtained through a review of the literature, consultations with key professionals, and an environmental scan of stroke resources available in four Atlantic provinces. Results: A learning resource that highlights important information related to the care of patients who have suffered a stroke was developed. It contained stroke background information, including warning signs for strokes, stroke types, risk factors, and deficits based on brain regions. The resource focuses on best practice stroke care including the importance of excellent collaboration of nursing with the members of the interprofessional team. Images, quizzes, and role play associated with stroke care were added to the resource to improve nursing orientation and to optimize patient care. Conclusion: The resource would be useful as a self-study tool to prepare nurses to work on a stroke unit. It could be shared with orientating nurses and it could be useful for experienced nurses to mentor others which could translate into better patient care in this area.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".