Observation of the Effect of TTM-Based Health Information Behavior Combined with Continuous Nursing on Cognitive and Motor Function, Living Ability, and the Quality of Life of Cerebral Stroke Patients
Post-publication record
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Bibliographic record
Abstract
Purpose. To discuss the effect of the transtheoretical model (TTM) of behavior-based health information behavior combined with continuous nursing on cognitive function, motor function, living ability, and quality of life of cerebral stroke (CS) patients. Methods. 540 cases of CS patients hospitalized in our hospital from June 2020 to June 2021 were selected. All the subjects were divided into the control group (270 cases) and study group (270 cases) according to the random number table. The control group was given routine nursing intervention and the study group was given TTM-based health information behavior combined with continuous nursing. The patients were paid a return visit 6 months after discharge, and their cognitive function, motor function, living ability, and quality of life were observed before and after intervention. Results. After intervention, the Montreal cognitive assessment scale score, Fugl-Meyer assessment of motor function score, Barthel index score, and short health scale score of both groups were higher than before intervention, and the study group was higher than the control group ( P < 0.05 ). Conclusion. TTM-based health information behavior combined with continuous nursing has a significant positive impact on cognitive function, motor function, living ability, and quality of life of CS patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".