The Impact of Doctor-Nurse Complementary Model Combined with Disease Tracking on Stroke Patients.
Bibliographic record
Abstract
Objective: The incidence of stroke worldwide is increasing year by year. With the enhancement of public health awareness, people's demand for the quality of stroke rehabilitation is getting higher and higher, so better quality care measures are needed in the treatment of stroke. Based on this, this paper explores the impact of a new type of nursing care measure, the complementary health care model combined with condition tracking, on stroke patients. Methods: 238 stroke patients were randomly divided into a conventional group (n=119) and a combined group (n=119). 238 stroke patients were randomly divided into conventional group (n=119) and combined group (n=119). The conventional group received routine care, in which doctors and nursing carried out their own work without cooperation after the patients were admitted to the hospital; the combined group received a complementary health care model and condition tracking, in which doctors and nurses jointly checked the rooms, discussed cases, jointly formulated treatments and nursing care plans, and jointly formulated the patients' discharge and rehabilitation plans after the patients were admitted to the hospital. Before the intervention, at the time of discharge, and 6 months after discharge, the neurological function of the patients in both groups was assessed using the National Institutes of Health Stroke Scale (NIHSS) and the Fugl-Meyer (FMA) scale, the cognitive function of the patients in both groups was assessed using the Montreal Cognitive Assessment (MoCA) scale and the Measured Mental State Examination (MMSE), and the cognitive function of the patients in both groups was assessed using the General Self-Efficacy Scale (GSS) and the Montreal Cognitive Assessment (MCA) scale. General Self-Efficacy Scale (GSES) to assess self-efficacy, Exercise Adherence Questionnaire (EAQ) to assess adherence to functional exercise and Specific Quality of Life Scale (SSQoL-12) to assess the quality of life of patients in both groups, and the self-developed satisfaction with nursing care to assess patients' satisfaction with the care model. Results: Before the intervention, there was no difference in the National Institutes of Health Stroke Scale (NIHSS), the Fugl-Meyer Assessment (FMA), the Montreal Cognitive Assessment (MoCA), the Mental State Examination (MMSE), the General Self-Efficacy Scale (GSES), the Exercise Adherence Questionnaire (EAQ) and the Stroke-Specific Quality of Life Scale-12 (SSQoL-12) scores between the two groups (P > .05). At discharge and six months later, NIHSS scores continued to decrease in both groups, with the joint group being lower than the conventional group (P < .05); scores for all other items continued to increase, with the joint being higher than the conventional group (P < .05). Satisfaction with care was higher in the combined group than in the conventional group (P < .05). Conclusion: The complementary healthcare model combined with condition tracking can effectively promote the prognosis of rehabilitation of stroke patients, and has a positive effect in promoting the recovery of neurological and cognitive functions, strengthening self-efficacy, and improving the quality of life, which can be promoted in the clinic.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".