Implementing An Online Education Program To Standardize Education For Stroke Nurses : From Pilot into Practice
Bibliographic record
Abstract
IntroductionA pilot project was conducted, prior to a provincial roll-out, to determine the applicability of an online program for standardizing education for stroke nurses. MethodsA pre-existing online education program with content consistent with the Canadian Stroke Best Practices was used. Staff from two stroke units (42 staff; 80% nurses) were recruited for the pilot project which consisted of completing six modules over a six-week period. A follow-up survey was completed at the end. The survey included questions on applicability to discipline and stroke experience.ResultsThe recommended modules were completed by 40 (95%) of the participants by the end of the pilot. All 42 participants (100%) completed the follow-up survey. Over 90% of participants indicated the modules were applicable to stroke unit, emergency, and intensive care nurses. Opinions on applicability for other disciplines varied. Participants indicated both new and experienced staff would benefit from the education. After the pilot, 180 stroke team members participated in the provincial roll-out. By the end of the license, 128 staff (75% nurses) had attempted at least one module. The recommended modules were successfully completed by 77% of users. An additional 15% completed between 1-5 modules, with 8% not completing any modules. Due to turnover or attrition, 52 seats were reassigned (with access still ongoing). ConclusionThe online education program is a viable option for standardized education for stroke nurses. Staff turnover and participant attrition can be a challenge, potentially highlighting the need to give short timelines and specific objectives to meet.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".