How Community Pharmacists Perceive Ethics in Clinical Research: A Qualitative Study.

In recent years, the importance of building evidence in clinical practice that is increasingly acknowledged globally has been recognized in Japan as well, and it is expected that clinical research by community pharmacists will grow. In Japan, however, community pharmacists have few opportunities to learn about research ethics and may lack the training to make ethical decisions. We conducted a questionnaire survey of community pharmacists (n = 200) using a free descriptive format to understand how they perceived research ethics. Our qualitative analysis of 170 respondents revealed various perspectives (, , and ) of Japanese pharmacists on ethics in the context of clinical research. With respect to how to understand research, the following perspectives were found: "research that prioritizes researchers," "research that prioritizes research subjects (patients)," and "research that enters into regular work." The perspectives on "research that prioritizes research subjects (patients)" and "research that enters into regular work" may inadvertently lead to ethically inappropriate research due to mismatch in professional values or poor understanding of research. These findings can contribute to the development of an educational program for community pharmacists on research ethics.

Teaching Research Ethics to Pharmacists: The Practice of Participatory Learning.

The research history of community pharmacists in Japan is short, and ethical responses may not be mature. Therefore, the Japan Pharmaceutical Association and universities are working on research ethics education to help pharmacists make appropriate ethical responses. In this study, we evaluated whether an educational program using participatory learning was effective in research ethics education for pharmacists. Regarding the educational effects of our workshop, the score for motivation to learn about research ethics was high, and that for judgment and applied skills related to research ethics was low. Overall, participants' assessment of the program contents was extremely favorable, indicating their satisfaction. Participatory learning was widely accepted and suggested to be effective in improving learning motivation. Additionally, to be able to apply the knowledge of research ethics to own research, it was considered necessary to continue learning through participatory learning. This will help pharmacists gain judgment and applied skills related to research ethics.

An Analysis of the Relationship between the Learning Process and Learning Motivation Profiles of Japanese Pharmacy Students Using Structural Equation Modeling.

Pharmacy students in Japan have to maintain strong motivation to learn for six years during their education. The authors explored the students’ learning structure. All pharmacy students in their 4th through to 6th year at Josai International University participated in the survey. The revised two factor study process questionnaire and science motivation questionnaire II were used to assess their learning process and learning motivation profiles, respectively. Structural equation modeling (SEM) was used to examine a causal relationship between the latent variables in the learning process and those in the learning motivation profile. The learning structure was modeled on the idea that the learning process affects the learning motivation profile of respondents. In the multi-group SEM, the estimated mean of the deep learning to learning motivation profile increased just after their clinical clerkship for 6th year students. This indicated that the clinical experience benefited students’ deep learning, which is probably because the experience of meeting with real patients encourages meaningful learning in pharmacy studies.

Effect of practical training on the learning motivation profile of Japanese pharmacy students using structural equation modeling.

PURPOSE: To establish a model of Japanese pharmacy students' learning motivation profile and investigate the effects of pharmaceutical practical training programs on their learning motivation. METHODS: The Science Motivation Questionnaire II was administered to pharmacy students in their 4th (before practical training), 5th (before practical training at clinical sites), and 6th (after all practical training) years of study at Josai International University in April, 2016. Factor analysis and multiple-group structural equation modeling were conducted for data analysis. RESULTS: A total of 165 students participated. The learning motivation profile was modeled with 4 factors (intrinsic, career, self-determination, and grade motivation), and the most effective learning motivation was grade motivation. In the multiple-group analysis, the fit of the model with the data was acceptable, and the estimated mean value of the factor of 'self-determination' in the learning motivation profile increased after the practical training programs (P= 0.048, Cohen's d= 0.43). CONCLUSION: Practical training programs in a 6-year course were effective for increasing learning motivation, based on 'self-determination' among Japanese pharmacy students. The results suggest that practical training programs are meaningful not only for providing clinical experience but also for raising learning motivation.

Artificial Neural Network Modeling of Quality of Life of Cancer Patients: Relationships between Quality of Life Assessments, as Evaluated by Patients, Pharmacists, and Nurses.

AIM: The purpose of this study was to investigate the difference between the professional perspectives of pharmacists and nurses in Japan with regard to evaluation of the quality of life (QOL) of cancer patients. METHODS: A group of cancer hospital inpatients (n=15) were asked to rate the condition of their health and their QOL by filling in a questionnaire. On the same day, a group of pharmacists (n=8) and nurses (n=18) also evaluated patient QOL. Three-layered artificial neural network (ANN) architecture was used to model the relationship between the different QOL evaluations made by patients, pharmacists, and nurses. RESULTS: Although there was no statistical difference between the QOL scores obtained from pharmacists and nurses, the correlation between these scores was weak (0.1188). These results suggest that pharmacists and nurses evaluate the QOL of their patients from different perspectives, based on their respective profession. QOL parameters were modeled with an ANN using the scores, given by patients in answer to questions regarding health-related QOL as input variables. Both the predictive performance of the ANN and the robustness of the optimized model were acceptable. The response surfaces calculated by ANN modeling showed that pharmacists and nurses evaluate patient's QOL using different information and reasoning, which is likely related to the nature of their contact with the patients. CONCLUSION: Health professionals evaluate patient QOL from different perspectives, depending on their profession.

Quantitative analysis of crystalline pharmaceuticals in tablets by pattern-fitting procedure using X-ray diffraction pattern.

A pattern-fitting procedure using an X-ray diffraction pattern was applied to the quantitative analysis of binary system of crystalline pharmaceuticals in tablets. Orthorhombic crystals of isoniazid (INH) and mannitol (MAN) were used for the analysis. Tablets were prepared under various compression pressures using a direct compression method with various compositions of INH and MAN. Assuming that X-ray diffraction pattern of INH-MAN system consists of diffraction intensities from respective crystals, observed diffraction intensities were fitted to analytic expression based on X-ray diffraction theory and separated into two intensities from INH and MAN crystals by a nonlinear least-squares procedure. After separation, the contents of INH were determined by using the optimized normalization constants for INH and MAN. The correction parameter including all the factors that are beyond experimental control was required for quantitative analysis without calibration curve. The pattern-fitting procedure made it possible to determine crystalline phases in the range of 10-90% (w/w) of the INH contents. Further, certain characteristics of the crystals in the tablets, such as the preferred orientation, size of crystallite, and lattice disorder were determined simultaneously. This method can be adopted to analyze compounds whose crystal structures are known. It is a potentially powerful tool for the quantitative phase analysis and characterization of crystals in tablets and powders using X-ray diffraction patterns.

Prediction of aminoglycoside response against methicillin-resistant Staphylococcus aureus infection in burn patients by artificial neural network modeling.

OBJECTIVE: To predict the response of aminoglycoside antibiotics (arbekacin: ABK) against methicillin-resistant Staphylococcus aureus (MRSA) infection in burn patients after considering the severity of the burn injury by using artificial neural network (ANN). Predictive performance was compared with logistic regression modeling. METHODOLOGY: The physiologic data and some indicators of the severity of the burn injury were collected from 25 burn patients who received ABK against MRSA infection. A three-layered ANN architecture with six neurons in the hidden layer was used to predict the ABK response. The response was monitored using three clinical criteria: number of bacteria, white blood cell count, and C-reactive protein level. Robustness of models was investigated by the leave-one-out cross-validation. RESULTS: The peak plasma level, serum creatinine level, duration of ABK administration, and serum blood sugar level were selected as the linear input parameters to predict the ABK response. The area of the burn after skin grafting was the best parameter for assessing the severity of the burn injury in patients to predict the ABK response in the ANN model. The ANN model with the severity of the burn injury was superior to the logistic regression model in terms of predicting the performance of the ABK response. CONCLUSION: Based on the patients' physiologic data, ANN modeling would be useful for the prediction of the ABK response in burn patients with MRSA infection. Severity of the burn injury was a parameter that was necessary for better prediction.

Structural equation modeling of qualification of pharmacists to improve subjected quality of life in cancer patients.

PURPOSE: To establish structural equation model (SEM) of subjected quality of life (QOL) in cancer patients taking into account qualification of pharmacists. METHOD: The SEM model was constructed from correlation matrix of the scores of answers of questions to both patients and pharmacists. Data were collected from 15 cancer patients who hospitalized and took opioid analgesics for pain control. The patients were asked 18 questions and pharmacists were asked seven questions. From the correlation matrix among scores of answers, a reasonable model was explored by SEM. RESULTS: Health-related QOL (HRQOL) in cancer patients can be modeled by latent variables consist of contributions from physical, emotional and functional domains. The fitting between data and the model was acceptable by statistical goodness-of-fit (GOF) index. The modeled HRQOL by SEM was weakly correlated with subjected QOL in patients, indicating that subjected QOL in patients would be affected not only by above latent variables but other variables. The model taking into account qualification of pharmacists to improve subjected QOL in patients was also made by SEM. The model was reasonably explained and fitting between data and the model was acceptable from some statistical index. The final model suggests that pharmacist can raise subjected QOL in patients through restraining unpleasant side effects. CONCLUSION: The qualification of pharmacists to improve subjected QOL in patients can be modeled by SEM. The final model suggests that pharmacists with qualification to assess patients' pain status contribute to raise subjected quality of life in cancer patients.

Artificial neural network modeling to predict the plasma concentration of aminoglycosides in burn patients.

The goal was to use an artificial neural network model to predict the plasma concentration of aminoglycosides in burn patients and identify patients whose plasma antibiotic concentration would be sub-therapeutic based on the patients' physiological data and taking into account burn severity. Physiological data and some indicators of burn severity were collected from 30 burn patients who received arbekacin. A three-layer artificial neural network with five neurons in the hidden layer was used to predict the plasma concentration of arbekacin. Linear modeling for prediction of plasma concentration and logistic regression modeling for the classification of patients were also used and the predictive performance was compared to results from the artificial neural network model. Dose, body mass index, serum creatinine concentration and amount of parenteral fluid were selected as covariates for the plasma concentration of arbekacin. Area of burn after skin graft was a good covariate for indicating burn severity. Predictive performance of the artificial neural network model including burn severity was much better than linear modeling and logistic regression analysis. An artificial neural network model should be helpful for the prediction of plasma concentration using patients' physiological data, and burn severity should be included for improved prediction in burn patients. Because the relationship between burn severity and plasma concentration of aminoglycosides is thought to be nonlinear, it is not surprising that the artificial neural network model showed better predictive performance compared to the linear or logistic regression models.