Psychosocial and Demographic Factors as Predictors of Attitude Towards Mental Illness by Caregivers in Federal Neuropsychiatric Hospital Aro Abeokuta
Idoko Joseph Onyebuchukwu,
Evbuoma Kikelomo Idowu,
Agoha Benedict Chico Emerenwa,
Oyeyemi Kunle
Issue:
Volume 1, Issue 1, November 2016
Pages:
1-8
Received:
14 July 2016
Accepted:
25 August 2016
Published:
19 October 2016
DOI:
10.11648/j.rs.20160101.11
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Abstract: This study investigated general health, self efficacy, social support and demographic variables as predictors of attitude to mental illness and care givers burden of psychiatric patients. For the study, five hypotheses were tested, one was confirmed, while four were partially confirmed. The study used 200 participants (89 (44.5%) males and 111 (55.5%) females) who are caregivers at the Federal Neuro-Psychiatric Hospital Aro-Abeokuta. An 89 item questionnaire was used to tap information on the care givers’ demographic and psychological variables. Multistage sampling technique was used. The study adopted multiple regression, 2x2x2x2x2 analysis of variance and Manova to test the significance of the demographic and psychosocial variables on attitude to mental illness and care givers burden of psychiatric patients.
Abstract: This study investigated general health, self efficacy, social support and demographic variables as predictors of attitude to mental illness and care givers burden of psychiatric patients. For the study, five hypotheses were tested, one was confirmed, while four were partially confirmed. The study used 200 participants (89 (44.5%) males and 111 (55....
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Clinical and Radiographic Indices as Correlates and Predictors of Self-Reported Physical Functions in Patients with Chronic Knee Osteoarthritis
Onigbinde Ayodele Teslim,
Olaoye Ayoola Olumide,
Lasisi Kamil
Issue:
Volume 1, Issue 1, November 2016
Pages:
9-15
Received:
20 September 2016
Accepted:
2 October 2016
Published:
27 October 2016
DOI:
10.11648/j.rs.20160101.12
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Abstract: It is unknown if there would be relationship between symptoms, radiographic changes and self-reported physical functions among patients with knee osteoarthritis (OA). The primary aims were to determine if there would be correlation between symptomatic and radiographic indices of knee OA, and physical function; and also determine if the indices are significant predictors of functional ability. 53 patients who had grade III knee OA participated in the study. The major test instruments were plain X-ray films and Western Ontario and Mcmaster University – WOMAC Osteoarthritis index Questionnaire. The Joint Space Width (JSW), inter-condylar thickening (ICT), tibia width (TW) and other measurements were measured using standard procedures. Descriptive statistics, Pearson’s product moment correlation, ANOVA and step-wise multiple regression analysis were used to summarize the data. Alpha level was set at p = 0.05. The mean WOMAC score was 33.78 ± 9.71. The duration of onset was 10.39 ± 7.14 months. Active knee flexion range of motion (AKFROM) was 102.71 (15.21) degrees while the medial and lateral JSW; ICT and TW were 0.51 ± 0.12cm, 0.74 ± 0.15cm, 1.09 ± 0.35cm and 7.01 ± 1.11cm respectively. There were significant correlations between WOMAC score; and AKFROM, PIA and passive KF (r = -0.37, p = 0.06; r = -0.32, p = 0.02; r = - 0.57, p = 0.001 respectively). There were also significant correlations between WOMAC score and lateral JSW (r = -0.31, p = 0.02), and TW (r = 0.37, p = 0.007) on plain radiograph. The result of the multiple regression analysis showed that the most significant predictor of functional capability was active knee flexion range of motion (F = 23.92, p = 0.001), contributing 31.9% to the prediction. It was concluded that pain intensities, active knee flexion range of motion, pain intensity, inter-condylar thickening, joint space and tibia widths were correlates of physical functions. Active Knee Flexion Range of Motion was the most significant predictor of self-reported functional capability of patients with knee OA.
Abstract: It is unknown if there would be relationship between symptoms, radiographic changes and self-reported physical functions among patients with knee osteoarthritis (OA). The primary aims were to determine if there would be correlation between symptomatic and radiographic indices of knee OA, and physical function; and also determine if the indices are ...
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Stroke Care and the Role of Big Data in Healthcare and Stroke
Lidong Wang,
Cheryl Ann Alexander
Issue:
Volume 1, Issue 1, November 2016
Pages:
16-24
Received:
11 September 2016
Accepted:
30 September 2016
Published:
25 November 2016
DOI:
10.11648/j.rs.20160101.13
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Abstract: Theoretically current electronic health data can now be securely linked on an unprecedented scale, potentially illuminating how diseases manifest and which treatments are best applied in the real world. Increasing volumes of information on real-time, actual patient experiences are now contained on social media and patient portal websites. Innovations and insight into the health care for individuals and entire populations can be gained when information from health monitors, genomic data, and clinical trial data is merged. In other words, we now have the theoretical technology to accumulate, store, convert, access, and evaluate massive amounts of data at a modest cost. Performance and clinical data from health care facilities, including clinics and hospitals, clinical research data by industry, and academic data from patient populations and the general public which may be generated through social media and/or other sources is included in big data. Just as access to sizable datasets evolves and becomes easier, analytical mistakes may occur more often and be easier to make lest rigorous standards and governance controls are employed. Indeed, it is more likely that improved analytics will also introduce us to at least a few more uncomfortable insights into the negligible value of some medicines. It is also noteworthy to mention that one common error is the assumption that the value of big data is within the data itself—its volume, accuracy, accessibility, “linkability,” etc. Unfortunately, despite the importance of the information, or the “bigger” the data, the greater the likelihood that this does not hold true. This review paper examines the relationship of big data to stroke care in a variety of stroke-related issues including: big data in stroke care, big data and visual analytics, big data in telecardiology, and some challenges and indications for future research.
Abstract: Theoretically current electronic health data can now be securely linked on an unprecedented scale, potentially illuminating how diseases manifest and which treatments are best applied in the real world. Increasing volumes of information on real-time, actual patient experiences are now contained on social media and patient portal websites. Innovatio...
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