Stroke Education for Nurses Through a Technology-Enabled Program
Bibliographic record
Abstract
Today's nurse faces many challenges in the workplace. Required to keep up in a constantly changing knowledge-based environment, he or she must balance complex professional responsibilities, staffing shortages, and increased acuity among the patient population. Continuing education must, therefore, be highly flexible and responsive to the personal and professional needs of the nurse learner. Technology-supported continuing education is suggested to be an appropriate way of meeting the learning needs of busy working nurses. The Stroke Best Practices for Nursing project used three complementary and integrated educational technologies-a-Web-based learning site, Web casting (live and archived), and two-way interactive videoconferencing--to deliver a minicourse focused on best practice stroke care to nurses working in northeastern and northwestern Ontario, a geographical area of approximately 600 km. In total, 96 nurses participated in the educational part of the program; 46 of the 96 (47%) took part in the assessment of the program. On the basis of this assessment strategy and the nurses' requests for other programs that do not use traditional face-to-face classrooms and lecture, the value of using educational technologies in health-based continuing education was strongly identified. This article describes key components of the project and celebrates the partnership among the organizing stakeholders: faculty in the school of nursing at the Laurentian University, the West Greater Toronto Area Stroke Network, and the Ontario Telemedicine Network. The article further describes findings related to the program's impact on participants' perceptions of competence as caregivers for stroke patients, participants' confidence using technology for educational purposes, and participants' satisfaction with the overall program.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".