The 2009 Helene Hudson Memorial Lectureship: Creating opportunities to support oncology nursing practice: Surviving and thriving
Bibliographic record
Abstract
There is a growing body of evidence to support that specialization in nursing leads to improved outcomes for patients, including increased QOL, improved symptom management, and fewer hospital admissions. Oncology nurses face several challenges in pursuing specialization, due to individual and system issues such as limited time and resources. To address these challenges, de Souza Institute launched a province-wide study group for nurses in Ontario who planned to write the Canadian Nurses Association (CNA) Oncology Certification Exam. The study group was led by educators from de Souza and Princess Margaret Hospital and drew expertise from nursing leaders across Ontario who shared the same vision of oncology nursing excellence. The study group was innovative by embracing telemedicine and web-based technology, which enabled flexibility for nurses' work schedules, learning styles, physical location and practice experience. The study group utilized several theoretical perspectives and frameworks to guide the curriculum: Adult Learning Theories, Cooperative Learning, Generational Learning Styles, CANO standards for practice and the CNA exam competencies. This approach enabled 107 oncology nurses across the province in 17 different sites to connect, as a group, study interactively and fully engage in their learning. A detailed evaluation method was utilized to assess baseline knowledge, learning needs, cooperative group process, exam success rates, and document unexpected outcomes. Ninety-four per cent of participants passed the CNA Oncology Exam. Lessons learned and future implications are discussed. The commitment remains to enable thriving through generating new possibilities, building communities of practice, mentoring nurses and fostering excellence in oncology practice.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".