Employees’ psychological contracts comprise their beliefs about what they have to contribute to their organizations and what inducements they will receive in return. One recommended approach to ...attract and retain employees is to design psychological contracts that allow them to contribute in desirable ways and receive attractive inducements. However, we know little about the factors that affect psychological contract preferences. We present a qualitative study on the preferred psychological contracts of employees who are in different career stages. Our findings reveal that the roles and self-concepts that employees take on at a particular career stage may shape preferences for stage-relevant contributions and inducements. These findings advance psychological contract theory by highlighting the plausible link between employees’ career stages and their psychological contract preferences.
This study compared the body contact pressure profiles of 2 types of mattresses: latex and polyurethane.
Twenty participants were required to lie down on the different mattresses in 3 different ...postures for 6 minutes, and their body contact pressure profiles were recorded with a pressure mat sensor.
The data indicated that the latex mattress was able to reduce the peak body pressure on the torso and buttocks and achieve a higher proportion of low-pressure regions compared with the polyurethane mattress.
Latex mattress reduced peak body pressure and achieved a more even distribution of pressure compared with polyurethane mattress across different sleeping postures.
The motion mimicry ability of patients facilitates execution of therapy moves based on visual observation of rehabilitation exercise videos, which can help speed up the recovery process. This study ...investigates the effects of visual feedback on the mimicking ability of human subjects in video-based rehabilitation. Inertial Measurement Unit (IMU) sensors was used, which provide a portable system to detect human motion tracking, allowing for experiments to be conducted without space restrictions and provide a greater variety of actions that can be tested. In the experiment, healthy subjects were shown a video of an instructor performing a certain movement task and had to mimic actions to the best of their ability. A real-time visual feedback system, based on input data from IMU sensors, was introduced to inform subjects of the accuracy of their mimicking actions. Subjects were tested with and without feedback and the relevant joint angle data was collected to determine the individual's mimicking ability. Our results showed a significant improvement in subject's mimicking ability from "no feedback" to "feedback" condition. The key implication of the findings is that visual feedback provides an extrinsic source that allows patients to better synchronize their hand-eye coordination during mimicry. Potential prospective works will investigate the relevance of motion mimicry mechanism in home-based rehabilitation.
The random-sum Poisson-Weibull variable is the sum of a random sample from a Weibull distribution with a sample size that is an independent Poisson random variable. It has a wide range of ...applications. This random sum is complex and difficult to analyze. Saddlepoint approximations are powerful tools for obtaining accurate expressions for closed-form distribution functions for these complex distributions. The use of saddlepoint approximations almost outperforms other methods with respect to computational costs, though not necessarily with respect to accuracy. This study introduces the saddlepoint approximations to the cumulative distribution function for the Poisson-Weibull model, from which the authors can obtain some important statistical measures of the central tendency of a cumulative distribution. They discuss the approximations of a random-sum variable by using dependent components, assuming the existence of a moment-generating function. The numerical examples of Poisson-Weibull random sums are presented.
This paper aims to compare and develop the influence on different sample sizes and sample ratios when using machine learning (ML) models, i.e., support vector machine (SVM) and artificial neural ...network (ANN), to produce landslide susceptibility maps (LSMs) in Penang Island, Malaysia. At the same time, traditional statistical (TS) models are also considered to produce LSMs in this comparative research. The receiver operating characteristic (ROC) curve and recall metric are applied to evaluate the model’s performance. Based on the evaluation criteria, the ML model outperforms the TS models and the ML models trained using the datasets with larger sample size give a better performance. ML models, especially SVM models, have better performance when training with balanced datasets as well as the datasets of more landslide sample data. Kruskal-Wallis test and Mann-Whitney
U
test are applied to test the significance. The results indicate that sample size and sample ratio are essential factors when considering ML models to produce LSMs. The LSMs produced in this research can provide valid and useful information to the local authorities for landslide mitigation and prediction.
Objective: This is a study of the quality of life (QOL) of 174 community-based chronic schizophrenia patients in Penang, Malaysia.
Method: The study samples were selected from the Out Patient ...Department, Department of Psychiatry, Penang General Hospital, Malaysia. The data was collected through personal interviews with the respondents. A questionnaire prepared by the research team was used to collect data on background characteristics. Lehman's (1988) Quality of Life Interview was used to collect data on patients’ QOL.
Result: Equal number of males and females participated in the study. The interviews on QOL indicated problems in the areas of life in general, place of living, daily activities, social relations, finance, work and general health. The results also revealed that community-based schizophrenia patients had acute poverty and experienced social isolation, discrimination and exploitation in the workplace.
Conclusion: Implications of these results on the implementation process of National Mental Health Policy in Malaysia are discussed. The research paper also discusses the negative impacts of limited rehabilitation facilities available in the community and its implications on the QOL of severely mentally ill patients. The need for immediate research attention on QOL of such patients in the South-east Asian region has been highlighted.
At present, much of the attention within tuberculosis (TB) management is spent on microbiological cure, and its impact on health-related quality of life (HRQoL) is either undervalued or seldom ...considered. The aim of this study was to evaluate the impact of TB treatment on HRQoL of new smear positive pulmonary tuberculosis (PTB) patients. Moreover, we also aimed to determine whether the selected socio-demographic and clinical variables were predictive of variability in the HRQoL scores over time.
This was a prospective follow-up of new smear positive PTB patients who were diagnosed at the chest clinic of Penang General Hospital between March 2010 and February 2011. All eligible patients (i.e., a new case of smear positive PTB, literate and aged 18 years or above) were asked to self-complete the SF-36v2 questionnaire at the start of their treatment, and then subsequently after the intensive phase and at the end of the treatment. A score on a health domain or component summary measure that was less than 47 norm-based scoring (NBS) point was considered indicative of impaired function within that health domain or dimension. Likewise, an individual having mental component summary (MCS) score ≤ 42 NBS point was considered to be at the risk of depression. Repeated measures ANOVA test was performed to examine how the summary scores varied over time, and to determine whether independent variables were predictive of variability in the physical component summary (PCS) and MCS scores over time.
A total of 216 patients completed the SF-36v2 questionnaire at the start of their treatment. Out of these, 177 and 153 completed the questionnaire at the second and third follow-ups, respectively. The mean PCS scores at the start of the treatment, after the intensive phase and at the end of treatment were 41.9 (SD 5.1), 45.8 (SD 4.8) and 46.0 (SD 6.9), respectively. Similarly, the mean MCS scores at the start of the treatment, after the intensive phase and at the end of the treatment were 39.9 (SD 7.3), 45.0 (SD 6.8) and 46.8 (SD 7.8), respectively. More than 23% of the patients were at the risk of depression at the end of their TB treatment. Patient's age and being a smoker were predictive of differences in the PCS scores. Similarly, monthly income, being a smoker and TB-related symptoms at the start of the treatment were predictive of differences in the MCS scores.
Although HRQoL improved with the treatment, the scores on component summary measures showed compromised physical and mental health among study patients even at the end of their TB treatment.
Abstract
Objective
To assess large language models on their ability to accurately infer cancer disease response from free-text radiology reports.
Materials and Methods
We assembled 10 602 computed ...tomography reports from cancer patients seen at a single institution. All reports were classified into: no evidence of disease, partial response, stable disease, or progressive disease. We applied transformer models, a bidirectional long short-term memory model, a convolutional neural network model, and conventional machine learning methods to this task. Data augmentation using sentence permutation with consistency loss as well as prompt-based fine-tuning were used on the best-performing models. Models were validated on a hold-out test set and an external validation set based on Response Evaluation Criteria in Solid Tumors (RECIST) classifications.
Results
The best-performing model was the GatorTron transformer which achieved an accuracy of 0.8916 on the test set and 0.8919 on the RECIST validation set. Data augmentation further improved the accuracy to 0.8976. Prompt-based fine-tuning did not further improve accuracy but was able to reduce the number of training reports to 500 while still achieving good performance.
Discussion
These models could be used by researchers to derive progression-free survival in large datasets. It may also serve as a decision support tool by providing clinicians an automated second opinion of disease response.
Conclusions
Large clinical language models demonstrate potential to infer cancer disease response from radiology reports at scale. Data augmentation techniques are useful to further improve performance. Prompt-based fine-tuning can significantly reduce the size of the training dataset.
Smart manufacturing has transformed the way decisions are made. By accelerating the delivery of data to the various decision points, more rapid decision-making processes can be realized. A generic ...Decision Support System (DSS) utilizes an efficient technique, which integrates the algorithm for inventive problem solving (ARIZ) and supervised machine learning into a model for supporting various automated decision making processes. The proposed model is to examine the theoretical framework of ARIZ by devising an ARIZ-based DSS model. It incorporates supervised ML algorithms to assist decision making processes. Three case studies from the manufacturing sector are evaluated. The results indicate the capability of the proposed DSS in achieving a high accuracy rate and, at the same time reducing the time and resources required for decision making. Our study has simplified the data processing and extraction processes through an automated ARIZ-based DSS model; therefore enabling a non-technical user the opportunity to harvest the vast knowledge from the collected data for efficient decision making.
Two main problems in landslide spatial prediction research are the lack of landslide samples (minority) to train the models and the misunderstanding of assigning equal costs to different ...misclassifications. In order to handle the problems properly, the research is conducted based on two main objectives, which are to augment the landslide sample data in an efficient way and to assign proper unequal costs to the two types of error when training and evaluating models. Resampling techniques, including random oversampling technique, synthetic minority oversampling technique and self-creating oversampling technique (SCOTE), are used to augment the minority class samples. Logistic regression (LR) and support vector machine (SVM) are used for landslide spatial classification. Receiver operating characteristic and cost curves are used to evaluate the models. The results show that the SVM models trained using the dataset generated by SCOTE with sample size of 10,000 have the best prediction performance. The nonparametric test, Kruskal–Wallis test, is used to test the difference of sample size between different groups, which shows that LR models are more sensitive to the change of sample size. Two landslide susceptibility maps are produced based on the models with the best prediction performance. The verification results show that the maps both successfully predict more than 86% of the susceptible area, which can provide valid information on landslide mitigation and prediction to the local authorities.
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