The Impact of Doctor-Nurse Complementary Model Combined with Disease Tracking on Stroke Patients.
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».