scholarly journals Reporting Inpatients’ Experiences and Satisfaction in a National Psychiatric Facility: A Study Based on the Random Forest Algorithm

2022 ◽  
Vol 9 ◽  
pp. 237437352110698
Author(s):  
Eman A. Haji ◽  
Ahmed H. Ebrahim ◽  
Hassan Fardan ◽  
Haitham Jahrami
Keyword(s):  
Random Forest ◽  
Psychiatric Inpatients ◽  
Random Forest Algorithm ◽  
Psychiatric Facility ◽  
Cross Sectional ◽  
Revisit Intention ◽  
High Satisfaction ◽  
Ease Of Access ◽  
Positive Word Of Mouth ◽  
Satisfaction Rates

Understanding psychiatric inpatients’ experiences is important to establish a culture of patient-centric care and promote trust in healthcare. This study aimed to evaluate nine dimensions of patients’ experiences and investigate their association with patient satisfaction, revisit intention, and positive word-of-mouth (WoM) recommendation. Cross-sectional questionnaire data from five years of surveying (2016–2020) in the main psychiatric hospital in Bahrain were statistically analyzed, involving 763 psychiatric inpatients with an overall 65.6 ± 17.2 length of stay (days). The findings show that across the five years 2016–2020, the overall reported satisfaction was “very high” (4.75 ± 0.44) with no significant differences between these five years (F [4, 758] = 0.66, p = 0.620). The experience of confidentiality received the highest rating (4.72 ± 0.45). The experiences of ease of access, hospitality quality, and quality of responsiveness to one's needs significantly correlated with revisit intention ( p ˂ 0.05). Patients with high satisfaction had greater potential for revisit intention (r [761] = 0.08, p = 0.027), which was associated with WoM recommendation (r [761] = 0.08, p = 0.033). Overall, men were less likely than women to experience convenient access to psychiatric wards. The findings of the Random Forest algorithm indicate the tendency of female patients with short-term stays to demonstrate lower satisfaction rates, and thus innovative approaches are needed when managing these groups’ psychiatric problems.

Changing perceptions of telemedicine in a surgical outpatients clinic

2022 ◽  
Vol 28 (1) ◽  
pp. 22-25
Author(s):  
Mark Sammut ◽  
Matthew Sammut ◽  
Daniel M Chircop ◽  
Kurt-Lee Chircop ◽  
Craig Muscat ◽  
...  
Keyword(s):  
Group Analysis ◽  
Healthcare Providers ◽  
Cross Sectional Study ◽  
Single Centre ◽  
Cross Sectional ◽  
Telephone Consultation ◽  
High Satisfaction ◽  
Patient Consultation ◽  
Satisfaction Rates

Background/Aims Before the COVID-19 pandemic, telemedicine was not widely used in surgical departments. Despite its increased use during the pandemic, there is a lack of data on the patient perspective. This study investigated patients' views of telemedicine in a surgical outpatients clinic setting. Method A single-centre cross-sectional study was performed, involving patients who were due to attend the surgical outpatients clinic of one surgical team. Independent investigators contacted the patients by telephone after their virtual telephone consultation to administer the questionnaire. Patient satisfaction rates were recorded using the PSQ-18 questionnaire. Patient consultation preferences were recorded and analysed. Results A total of 223 patients participated in this study. The majority of patients' perceptions shifted in favour of virtual consultations after the onset of the pandemic (P<0.05). Sub-group analysis showed no significant differences between the preferences of older and younger patients before or after the onset of the pandemic. Overall, patients reported high satisfaction rates with their virtual consultations. Conclusions Patient perceptions are changing in favour of virtual telemedicine consultations. Training healthcare providers in this method of service delivery is essential to maintain a good quality of care.

scholarly journals Evaluasi kepuasan kerja perawat di ruang MPKP, persiapan mpkp dan non MPKP

2020 ◽  
Vol 12 (2) ◽  
pp. 143-148
Author(s):  
Ibrahim Rahmat ◽  
Maulina Nugraheni ◽  
Sri Werdati
Keyword(s):  
Job Satisfaction ◽  
Delivery System ◽  
Nursing Care ◽  
Care Delivery ◽  
Cross Sectional ◽  
High Satisfaction ◽  
Descriptive Approach ◽  
Satisfaction Rates ◽  
Statistical Results

Abstract: This study aims to determine differences in job satisfactionof nurses in three rooms with a nursing care delivery system by themethod of PNPM, Preparation and non PNPM PNPM GrhasiaHospital Yogyakarta. The method used was a cross sectional studywith comparative descriptive approach. The results show that nursesat PNPM is largely at the level of job satisfaction is high (77.78%).Meanwhile, in the preparation of non-PNPM PNPM and 100% ofnurses are in high satisfaction rates. The statistical results showed nodifference in job satisfaction between PNPM, PNPM preparation,and non PNPM.

METHOD SUGGESTING CITY WALKING ROUTES FOR PEDESTRIANS USING AN EXAMPLE OF SAINT-PETERSBURG

2019 ◽  
pp. 71-76
Author(s):  
A.E. Semenov
Keyword(s):  
Random Forest ◽  
Develop Model ◽  
Random Forest Algorithm ◽  
Saint Petersburg ◽  
Pedestrian Navigation ◽  
Factors Influencing ◽  
The City

The method of pedestrian navigation in the cities illustrated by the example of Saint-Petersburg was investigated. The factors influencing people when they choose a route for their walk were determined. Based on acquired factors corresponding data was collected and used to develop model determining attractiveness of a street in the city using Random Forest algorithm. The results obtained shows that routes provided by the method are 14% more attractive and just 6% longer compared with the shortest ones.

Classification and photometric redshift estimation of quasars in photometric surveys

2020 ◽  
Vol 15 (S359) ◽  
pp. 40-41
Author(s):  
L. M. Izuti Nakazono ◽  
C. Mendes de Oliveira ◽  
N. S. T. Hirata ◽  
S. Jeram ◽  
A. Gonzalez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Nearest Neighbour ◽  
Random Forest Algorithm ◽  
Photometric Redshift ◽  
Using Data

AbstractWe present a machine learning methodology to separate quasars from galaxies and stars using data from S-PLUS in the Stripe-82 region. In terms of quasar classification, we achieved 95.49% for precision and 95.26% for recall using a Random Forest algorithm. For photometric redshift estimation, we obtained a precision of 6% using k-Nearest Neighbour.

scholarly journals Exploring the relationship between students' learning satisfaction and self-efficacy during the emergency transition to remote learning amid the coronavirus pandemic: A cross-sectional study

2021 ◽  
Author(s):  
Monira I. Aldhahi ◽  
Abdulfattah S. Alqahtani ◽  
Baian A. Baattaiah ◽  
Huda I. Al-Mohammed
Keyword(s):  
Online Learning ◽  
Time Management ◽  
Self Efficacy ◽  
Cross Sectional Study ◽  
Cross Sectional ◽  
Learning Satisfaction ◽  
High Satisfaction ◽  
E Learning ◽  
Students Satisfaction ◽  
Remote Learning

AbstractThe overarching objective of this study was to assess learning satisfaction among students and to determine whether online-learning self-efficacy was associated with online learning satisfaction during the emergency transition to remote learning. This cross-sectional study involved a survey distributed to 22 Saudi Arabian universities. The survey used in this study consisted of an online learning self-efficacy (OLSE) questionnaire and an electronic learning (e-learning) satisfaction questionnaire. A total of 1,226 respondents voluntarily participated in and completed the survey. Students in medical fields made up 289 (23.6%). A Kruskal–Wallis H test and a chi-square test were used to compare the student’s satisfaction based on the educational variables. Spearman’s correlation and multiple linear regression analyses were performed to assess the association between self-efficacy and satisfaction. The findings revealed degrees of satisfaction ranging between high satisfaction and dissatisfaction. The majority of students (51%) expressed high satisfaction, and 599 students (49%) reported experiencing a low level of satisfaction with e-learning. A comparison of groups with low and high satisfaction scores revealed a significant difference in the OLSE. High satisfaction was positively correlated with the OLSE domains: time management, technology, and learning. The OLSE regression analysis model significantly predicted satisfaction. It showed that the model, corrected for education level and grade point average of the students, significantly predicted e-learning satisfaction (F = 8.04, R2 = 0.59, p = .004). The study concluded that students’ satisfaction with the e-learning experience is influenced by e-learning self-efficacy. The study’s findings lead to the practical implications and identify the need to improve the remote learning, time management and technology self-efficacy to enhance students’ satisfaction.

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