Human Care Theory and Influences on the Life Quality Index of Cancer Patients in Household Life
Bibliographic record
Abstract
To investigatetheinfluences of the application of human care theory on the life quality and happiness of cancer patients after they receiveda community nursing care which was implemented by the human care theory. The quality life and the happiness index of 93 patients with cancer living in the six communities in Jillin were assessed, the assessment of the life quality was based on a life quality scale (SF-36) and that of the happiness index was based on Memorial University of Newfoundland Scale of Happiness (MUNSH). The community nurses cared for these patients by applying the theory of human care and the life quality and the happiness index of the patients were observed after the care. The results showed that there were significant differences in the score of 5 dimensions in the eight dimensions of the life quality between before the care and after the care (<0.05), and there were significant differences in the average sores of the positive emotion, positive experience, negative emotion, negative experience and level of happiness included in the happiness index between before the care and after the care (<0.05), suggesting that the theory of human care can be used for the care of patients with cancer and the application of the theory can effectively improve the life quality and the happiness index of the patients, strengthen their problem-solving abilities and let them have a positive attitude towards their lives.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".