Prince Edward Island: Building Capacity – The Implementation of a Critical Care/Emergency Program
Bibliographic record
Abstract
Like other Canadian provinces, Prince Edward Island has a shortage of experienced nurses, especially in critical and emergency care. To increase the numbers of competent nurses, a PEI-based nursing course in these areas was identified as key to building capacity. This Research to Action pilot program successfully involved nurses in PEI-based emergency and critical care courses developed by the Nova Scotia Registered Nurses Professional Development Centre and funded by Human Resources and Skills Development Canada. The programs were offered on a full-time basis, lasted 14 weeks and included classroom and simulation laboratory time, along with a strong clinical component.Sixteen RNs graduated from the courses and became Advanced Cardiovascular Life Support (ACLS) certified. An additional 12 RNs were trained as preceptors. Feedback from participants indicates greater job satisfaction and increased confidence in providing patient assessments and care. Based on the program's success, the RTA partners proposed the establishment of an ongoing, PEI-based critical care and emergency nursing program utilizing 80/20 staffing models and mentorship. Their proposal was approved, with courses set to resume in January, 2012.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".