Cultural and Ethical Barriers to Cancer Treatment in Nursing Homes and Educational Strategies: A Scoping Review
Bibliographic record
Abstract
(1) Background: The aging of the population, the increase in the incidence of cancer with age, and effective chronic oncological treatments all lead to an increased prevalence of cancer in nursing homes. The aim of the present study was to map the cultural and ethical barriers associated with the treatment of cancer and educational strategies in this setting. (2) Methods: A systematic scoping review was conducted until April 2021 in MEDLINE, Embase, and CINAHL. All articles assessing continuum of care, paramedical education, and continuing education in the context of older cancer patients in nursing homes were reviewed. (3) Results: A total of 666 articles were analyzed, of which 65 studies were included. Many factors interfering with the decision to investigate and treat, leading to late- or unstaged disease, palliative-oriented care instead of curative, and a higher risk of unjustified transfers to acute care settings, were identified. The educational strategies explored in this context were generally based on training programs. (4) Conclusions: These results will allow the co-construction of educational tools intended to develop knowledge and skills to improve diagnostic and therapeutic decision-making, the consistency of care, and, ultimately, the quality of life of older cancer patients in nursing homes.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".