Factors Related to Person-Centered Care for Older Patients With Cancer and Dementia in Designated Cancer Hospitals
Bibliographic record
Abstract
BACKGROUND: Person-centered care (PCC) should be promoted for patients with cancer and dementia who are likely to be hindered from pursuing a meaningful life owing to their will not being reflected in the cancer treatment process. OBJECTIVE: This study aimed to clarify the factors related to nurses' practice of PCC for older patients with cancer and dementia in designated cancer hospitals. METHODS: An online cross-sectional survey was administered to nurses working at designated cancer hospitals in Japan. The survey items included demographic data and factors assumed to be related to nursing practice and practice of PCC. RESULTS: A multiple regression analysis indicated that the factors related to the practice of PCC were attitude toward patients with dementia (β = 0.264, P < .001), holding conferences (β = 0.255, P < .001), knowledge about cancer nursing (β = 0.168, P < .001), knowledge about dementia (β = 0.128, P = .003), and participation in dementia care training (β = 0.088, P = .032). CONCLUSIONS: Nurses' practice of PCC may not be sufficient to provide personalized care tailored to patients' cognitive function. The factors related to PCC are attitude toward patients with dementia, holding conferences, knowledge about cancer and dementia, and dementia care training. IMPLICATIONS FOR PRACTICE: To promote PCC for patients with cancer and dementia, nurses should learn about these conditions with an interest in patients and collaborate with other professionals. Future studies should use cluster sampling and focus on the extent of cancer or dementia symptoms.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".