Bibliographic record
Abstract
Recruitment and retention issues associated with the growing nursing shortage in Canada are magnified in Nunavut, where the scope of nursing practice is much broader than in urban settings. The Qikiqtani General Hospital (QGH), a 35-bed hospital in Nunavut's capital, Iqaluit, was the home base for this multi-pronged pilot project that spanned 16 months to March 2011. The goals of the project included creating opportunities for front-line nurses to develop new clinical skills and knowledge and expand their competencies; offering enhanced critical care training relevant to the needs of nurses; and providing a smooth transition to entry to practice in a hospital setting for new graduate nurses. An in-house mentorship program was developed, and contracts were made with three outside parties: the Critical Care Education Network (CRI), the Ottawa Hospital and the Perinatal Partnership Program of Eastern and Southeastern Ontario. A number of professional development opportunities were provided – for example, 26 nurses participated in the CRI's critical care training, and six nurses were trained as CRI trainers.Overall, nurses were satisfied with the accessibility, delivery and applicability of the RTA education opportunities, and all nurses agreed that these opportunities increased their professional skills. A plan for the sustainability of the critical care portion of the Nunavut RTA project is currently in place, and the QGH is in the process of hiring a nurse educator for the hospital. This hiring will be a key piece to sustain the project initiatives. If the mentorship program is to continue, it will be essential to hire someone dedicated to the orientation of new graduates and new nurses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".