Building A High Quality Oncology Nursing Workforce Through Lifelong Learning: The De Souza Model
Bibliographic record
Abstract
AbstractCancer is one of the leading causes of death in the world. Along with increased new cases, cancer care has become increasingly complex due to advances in diagnostics and treatments, greater survival, and new models of palliative care. Nurses are a critical resource for cancer patients and their families. Their roles and responsibilities are expanding across the cancer care continuum, calling for specialized training and support. Formal education prepares nurses for entry level of practice, however, it does not provide the specialized competencies required for quality care of cancer patients. There is urgent need to align the educational system to the demands of the health care system, ease transition from formal academic systems to care settings, and to instill a philosophy of lifelong learning. We describe a model of education developed by de Souza Institute in Canada, based on the Novice to Expert specialty training framework, and its success in offering structured oncology continuing education training to nurses, from undergraduate levels to continued career development in the clinical setting. This model may have global relevance, given the challenge in managing the demand for high quality care in all disease areas and in keeping pace with the emerging advances in technologies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".