Measurement of Patients' Perceptions of the Quality of Acute Stroke Services: Development and Validation of the STROKE Perception Report

Alexandrov, Anne W.; Brewer, Barbara B.; Moore, Kari; Grau, Charlene; Beenstock, Dana J; Cudlip, Fern; Murphy, Deborah; Klassman, Lynn; Korsnack, Andrea; Johnson, Brenda; Parliament, Connie; Lane, Patricia A.; Walsh, Tina; Gottbrath, Deidra; Wojner, Alexis J; DiBiase, Stephen E; Alexandrov, Andrei V.

doi:10.1097/jnn.0000000000000471

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…Specialised and advanced stroke services are out of reach for patients with low monthly incomes and those who live in rural areas (Alexandrov et al, 2019; Wu et al, 2019). Patients suffering from worse physical functions may be the reason for the high level of stigma.…”

Section: Discussionmentioning

confidence: 99%

Correlates of stigma for poststroke patients: A meta‐analysis

Yang

et al. 2022

Journal of Clinical Nursing

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Aims A meta‐analysis was carried out to review and identify correlates of stigma in poststroke patients. Background Patients suffer from impaired physical functions and sequelae of stroke. Stroke‐related stigma hinders the recovery process and produces poorer clinical outcomes of stroke, leading to compromised quality of life. Design A systematic review and meta‐analysis was reported by following PRISMA 2020 guidelines. Review Methods Nine databases were searched from their inception to May 2021 to identify studies focused on the relationships among demographics, disease‐related factors, psychosocial factors and poststroke stigma. Included studies were assessed by using the Agency for Healthcare Research and Quality (AHRQ) scale. The statistical software R studios were used to perform statistical analysis. Results Nineteen studies were included in the meta‐analysis. Four demographic factors (age, caregiver, residence, monthly income), seven stroke‐related characteristics (type of stroke, mRS, ADLs, stroke duration, recurrence, sequelae, chronic disease comorbidity) and three psychosocial factors (depression, social support, quality of life) showed significant associations with stroke‐related stigma. Conclusions The results of our study can serve as a foundation for designing interventions to reduce stigma and improve the overall quality of life of poststroke patients and may produce positive clinical outcomes. Healthcare professionals should be aware of stroke patients who are characterised by correlates of stigma. Relevance to clinical practice Poststroke patients showed a moderate‐to‐high level of stigma, and this issue warrants more attention. This review provides a preliminary foundation for healthcare professionals to develop interventions to address stroke‐related stigma by focusing on demographic, disease‐related and psychosocial factors. Additionally, stigma identification should be a part of clinical nursing evaluation. The involvement of clinical and community nursing is very important to screen stroke‐related stigma and pay attention to this population.

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Section: Discussionmentioning

confidence: 99%

Correlates of stigma for poststroke patients: A meta‐analysis

Yang

et al. 2022

Journal of Clinical Nursing

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“…These interventions must encompass measures designed to enhance and assess levels of physical activity and participation since these aspects often remain suboptimal even when an individual's functional recovery appears satisfactory [13]. Importantly, patients' perspectives on the quality of healthcare have gained prominence as crucial components of quality assessment, extending beyond standardized clinical outcomes [14].…”

Section: Disparitiesmentioning

confidence: 99%

A Comparative Study Investigating Machine Learning Methods for EMG Data Classification in Post-Stroke Rehabilitation

M. Abdulaziz,

Loqman

2024

IRJIET

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Physiotherapy is essential for boosting recovery and enhancing quality of life after a stroke. Individualized therapies are required for stroke rehabilitation. This work explores machine learning techniques for electromyography (EMG) data classification in the context of post-stroke rehabilitation, which is important for comprehending and improving motor function. Our analysis covers a wide range of methods, such as Gradient Boosting (GB), Histogram-Based Gradient Boosting, Cat Boost, K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Linear Discriminant Analysis (LDA), and LDA. We measure their performance using criteria like accuracy, precision, recall, and F1-score through a thorough evaluation procedure. Our results show that DT and RF perform consistently better than other algorithms, proving their reliability and effectiveness in the classification of EMG data. But KNN is equally promising, with relatively lesser precision than LDA. The work emphasizes how decision-based algorithms and ensemble methods may effectively classify EMG data, which has ramifications for improving stroke rehabilitation techniques. These discoveries can be used by scholars and professionals to further the creation of machine learning-based instruments for accurate gesture recognition in post-stroke rehabilitation.

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“…In contrast, patients perceive them as lacking in caring behaviors (Calong & Soriano, 2018). Other dimensions of caring attitudes in the Emergency Department (ED) include how nurses administer treatment and the sincerity of their demeanor (Alexandrov et al, 2019). High-quality, professional nursing care has enhanced patient and family satisfaction (Mobolaji-Olajide et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Nurses' Perception of Caring-Based Nursing Care for Stroke Patients with Thrombolysis and Influencing Factors

Mulyatsih,

Nursalam,

Sriyono

et al. 2023

J. of Law and Sust. Develop.

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Background: Nursing care based on caring can improve the outcomes of post-stroke patients. However, many nurses tend to prioritize technical tasks over caring behaviors. The factors causing the lack of caring attitudes among nurses in providing care to stroke patients in hospitals are not yet known. Objective: To determine nurses' perceptions of caring-based nursing care for stroke patients with thrombolysis and the influencing factors. Methods: A quantitative research design using descriptive analysis with a cross-sectional approach was employed. Inclusion criteria included practicing nurses aged 20-50 with at least one year of work experience. Exclusion criteria were nurses who were sick, on leave, or engaged in educational assignments. Sample selection was done using proportional stratified random sampling over two weeks in September 2022. Results: A total of 158 respondents from three hospitals completed the questionnaire. Most nurses had good knowledge about assessment, nursing diagnosis, intervention, implementation, and evaluation (80.4%, 69.6%, 63.3%, 58.9%, and 69.6%, respectively). The ability to assess the NIHSS was also good (72.2%), but the ability to measure mRS was less satisfactory (52.5%). The results of the Coefficient of Determination (R2) test showed that the contribution of the factors of nurses, information, organization, and job to the nursing care of stroke patients was 50.3%. The direct hypothesis results also showed a significant influence of nurses, information, and job factors on caring-based nursing care for stroke patients (t-statistic value > 1.96 and p-value < 0.05). The better the nurse, information, and job factors, the more likely it is to improve nurses' caring attitudes (positive coefficient values). Conclusion: Nurses' knowledge about caring-based nursing care for stroke patients still needs improvement. Nurse, information, and job factors significantly influence caring-based nursing care for stroke patients with thrombolysis.

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Measurement of Patients' Perceptions of the Quality of Acute Stroke Services: Development and Validation of the STROKE Perception Report

Cited by 8 publications

References 11 publications

Correlates of stigma for poststroke patients: A meta‐analysis

Correlates of stigma for poststroke patients: A meta‐analysis

A Comparative Study Investigating Machine Learning Methods for EMG Data Classification in Post-Stroke Rehabilitation

Nurses' Perception of Caring-Based Nursing Care for Stroke Patients with Thrombolysis and Influencing Factors

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