Study on Nursing Effect of Individualized Health Education Combined with Cognitive Training in Elderly Stroke Patients
Bibliographic record
Abstract
Objective To study the nursing effect of individualized health education combined with cognitive training in elderly stroke patients. Methods 112 elderly stroke patients treated in the Department of Neurology of our hospital from January to June 2017 were randomly divided into experimental group and control group. In the control group, routine treatment, nursing and rehabilitation exercises were carried out, and regular health education was carried out. The experimental group, on the basis of routine treatment, nursing and rehabilitation exercise, carried out individualized health education and cognitive training combining the content of individualized health education. The effect of intervention was evaluated by Montreal Cognitive Assessment Scale (MoCA), health education questionnaire, daily life self-care scale (ADL) and nurse job satisfaction questionnaire before intervention, at the end of intervention for 4 weeks and at the end of intervention for 12 weeks. Results At the end of 4th and 12th week after intervention, the scores o MoCA, the awareness rate of health education, ADL and job satisfaction of nurses were higher than those before intervention, and the observation group was higher than that of the control group (P<0.05), with statistical difference (P<0.05). Conclusion Individualized health education combined with cognitive training can effectively improve the patients’ cognitive function, improve the patients’ awareness of stroke disease and the compliance of health education, improve the patients’ self-care ability in daily life and the satisfaction of nursing work, so as to improve the patients’ quality of life and quality of living.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".