Mobilizing purpose and passion in oncology nursing care of older adults: From conference workshop to special interest group
Bibliographic record
Abstract
In Canada, 45% of new cancer cases and 63% of cancer deaths occur amongst Canadians 70 years and older. These older people with cancer and their families present particular needs and concerns that often remain under-recognized and unmet. As the number of older Canadians is expected to more than double in the next 25 years, we must integrate understanding of aging into oncology nursing practice, education, policy, and research, developing models of care that optimize appropriate outcomes for older adults. We present the Canadian Association of Nurses in Oncology (CANO) Oncology and Aging Special Interest Group (SIG), as an initiative to mobilize oncology nurses in addressing these concerns. In an overview of the 2015 CANO conference workshop that launched this group, we highlight practice concerns and priorities identified through interactive discussion with participants. We also describe development of the SIG since 2015, including objectives that will define next steps.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".