The development of anti-cancer programs in Canada for the geriatric population: an integrated nursing and medical approach
Bibliographic record
Abstract
Cancer control in Canada refers to the development of comprehensive programs utilising modern techniques, tools and approaches that actively prevent, cure or manage cancer. The scope of such programs is quite vast. They range from prevention, early detection and screening, comprehensive treatment both curative and palliative to comprehensive palliative care. Cancer is a disease associated with the aging population, and as the population ages the incidence of cancer would be expected to rise as well. This in itself poses a great challenge. In addition, the aging population demographics with the projected rise in the numbers of senior citizens, especially the over 80 group in the next decade, poses its own creative challenges to health planners. In Canada, health care is centrally administered, and controlled by the provincial governments of Canada, under the Canada Health Act. The challenge of developing comprehensive programs for the geriatric population requires changes in the care models and care pathways. The patient-centred models that have been adapted require a multidisciplinary approach to the clientele and their families that integrates cancer therapy and geriatric care and realities. This requires changes in the nursing and medical approach, as well as education in the subtleties of the two intersecting medical realities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".