Intelligent rehabilitation training in nursing intervention for improving negative emotion, self-efficacy, and quality of life of patients with craniocerebral injury
Bibliographic record
Abstract
Objective To analyze the improvement of negative emotion, self-efficacy, and quality of life of patients with craniocerebral injury by intelligent rehabilitation in nursing intervention training. Methods 146 patients with mild to moderate craniocerebral injury admitted to our hospital from January, 2018 to January, 2019 were selected as study objects, and were randomly divided into an observation group and a control group, with 73 cases in each group. The control group were given routine nursing intervention, and the observation group intelligent rehabilitation training. After 3 months of follow-up, the rehabilitation effects of the two groups were compared. The quality of life was assessed by modified Barthel Index Score (BI) and Montreal Cognitive Assessment Scale (MOCA). The self-efficacy was assessed by General Self-efficacy Scale (GSES). The self-rating anxiety scale (SAS) and self-rating depression scale (SDS) were used to evaluate negative emotions. The quality of life, negative emotions, self-efficacies, and nursing satisfaction of the two groups before and after nursing care were compared. Results The total effective rate was significantly higher in the observation group than in the control group (91.78% vs. 71.23%, P<0.05). The scores of BL, MOCA, GSES, SAS, and SDS were (78.52±0.16), (28.64±2.79), (3.64±0.79), (49.91±1.70), and (48.15±1.63) in the observation group, which were higher than those in the control group (all P<0.05). Conclusion Intelligent rehabilitation training for patients with craniocerebral injury can improve their rehabilitation effect, quality of life, cognitive function, and self-efficacy, and alleviate their anxiety and depression. Key words: Intelligent rehabilitation training; Craniocerebral injury; Emotion; Self-efficacy; Quality of life; Nursing intervention
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".