Comparison of Subjective Facial Emotion Recognition and "Facial Emotion Recognition Based on Multi-Task Cascaded Convolutional Network Face Detection" between Patients with Schizophrenia and Healthy Participants.

Patients with schizophrenia may exhibit a flat affect and poor facial expressions. This study aimed to compare subjective facial emotion recognition (FER) and FER based on multi-task cascaded convolutional network (MTCNN) face detection in 31 patients with schizophrenia (patient group) and 40 healthy participants (healthy participant group). A Pepper Robot was used to converse with the 71 aforementioned participants; these conversations were recorded on video. Subjective FER (assigned by medical experts based on video recordings) and FER based on MTCNN face detection was used to understand facial expressions during conversations. This study confirmed the discriminant accuracy of the FER based on MTCNN face detection. The analysis of the smiles of healthy participants revealed that the kappa coefficients of subjective FER (by six examiners) and FER based on MTCNN face detection concurred (κ = 0.63). The perfect agreement rate between the subjective FER (by three medical experts) and FER based on MTCNN face detection in the patient, and healthy participant groups were analyzed using Fisher's exact probability test where no significant difference was observed (p = 0.72). The validity and reliability were assessed by comparing the subjective FER and FER based on MTCNN face detection. The reliability coefficient of FER based on MTCNN face detection was low for both the patient and healthy participant groups.

Investigation of Methods to Create Future Multimodal Emotional Data for Robot Interactions in Patients with Schizophrenia: A Case Study.

Rapid progress in humanoid robot investigations offers possibilities for improving the competencies of people with social disorders, although this improvement of humanoid robots remains unexplored for schizophrenic people. Methods for creating future multimodal emotional data for robot interactions were studied in this case study of a 40-year-old male patient with disorganized schizophrenia without comorbidities. The qualitative data included heart rate variability (HRV), video-audio recordings, and field notes. HRV, Haar cascade classifier (HCC), and Empath API© were evaluated during conversations between the patient and robot. Two expert nurses and one psychiatrist evaluated facial expressions. The research hypothesis questioned whether HRV, HCC, and Empath API© are useful for creating future multimodal emotional data about robot-patient interactions. The HRV analysis showed persistent sympathetic dominance, matching the human-robot conversational situation. The result of HCC was in agreement with that of human observation, in the case of rough consensus. In the case of observed results disagreed upon by experts, the HCC result was also different. However, emotional assessments by experts using Empath API© were also found to be inconsistent. We believe that with further investigation, a clearer identification of methods for multimodal emotional data for robot interactions can be achieved for patients with schizophrenia.

Characteristics of interactive communication between Pepper robot, patients with schizophrenia, and healthy persons.

Background: Expressing enjoyment when conversing with healthcare robots is an opportunity to enhance the value of human robots with interactive capabilities. In clinical practice, it is common to find verbal dysfunctions in patients with schizophrenia. Thus, interactive communication characteristics may vary between Pepper robot, persons with schizophrenia, and healthy persons. Objective: Two case studies aimed to describe the characteristics of interactive communications, 1) between Pepper as a healthcare robot and two patients with schizophrenia, and 2) between Pepper as a healthcare robot and two healthy persons. Case Report: The "Intentional Observational Clinical Research Design" was used to collect data. Using audio-video technology, the conversational interactions between the four participants with the Pepper healthcare robot were recorded. Their interactions were observed, with significant events noted. After their interactions, the four participants were interviewed regarding their experience and impressions of interacting with the Pepper healthcare robot. Audio-video recordings were analyzed following the analysis and interpretation protocol, and the interview data were transcribed, analyzed, and interpreted. Discussion: There were similarities and differences in the interactive communication characteristics between the Pepper robot and the two participants with schizophrenia and between Pepper and the two healthy participants. The similarities were experiences of human enjoyment while interacting with the Pepper robot. This enjoyment was enhanced with the expectancy of the Pepper robot as able to entertain, and possessing interactive capabilities, indicating two-way conversational abilities. However, different communicating characteristics were found between the healthy participants' impressions of the Pepper robot and the participants with schizophrenia. Healthy participants understood Pepper to be an automaton, with responses to questions often constrained and, on many occasions, displaying inaccurate gaze. Conclusion: Pepper robot showed capabilities for effective communication pertaining to expressing enjoyment. The accuracy and appropriateness of gaze remained a critical characteristic regardless of the situation or occasion with interactions between persons with schizophrenia, and between healthy persons. It is important to consider that in the future, for effective use of healthcare robots with multiple users, improvements in the areas of the appropriateness of gaze, response time during the conversation, and entertaining functions are critically observed.

Development Issues of Healthcare Robots: Compassionate Communication for Older Adults with Dementia.

Although progress is being made in affective computing, issues remain in enabling the effective expression of compassionate communication by healthcare robots. Identifying, describing and reconciling these concerns are important in order to provide quality contemporary healthcare for older adults with dementia. The purpose of this case study was to explore the development issues of healthcare robots in expressing compassionate communication for older adults with dementia. An exploratory descriptive case study was conducted with the Pepper robot and older adults with dementia using high-tech digital cameras to document significant communication proceedings that occurred during the activities. Data were collected in December 2020. The application program for an intentional conversation using Pepper was jointly developed by Tanioka's team and the Xing Company, allowing Pepper's words and head movements to be remotely controlled. The analysis of the results revealed four development issues, namely, (1) accurate sensing behavior for "listening" to voices appropriately and accurately interacting with subjects; (2) inefficiency in "listening" and "gaze" activities; (3) fidelity of behavioral responses; and (4) deficiency in natural language processing AI development, i.e., the ability to respond actively to situations that were not pre-programmed by the developer. Conversational engagements between the Pepper robot and patients with dementia illustrated a practical usage of technologies with artificial intelligence and natural language processing. The development issues found in this study require reconciliation in order to enhance the potential for healthcare robot engagement in compassionate communication in the care of older adults with dementia.

Nurses' perception regarding patient safety climate and quality of health care in general hospitals in Japan.

AIM: To clarify perceptions of nurses towards patient safety climate and quality of health care in Japan. BACKGROUND: Nurses' perceptions of patient safety climate and quality of health care services are not well-known. METHOD: The survey was conducted at general hospitals with 200 beds or more using the Patient Safety Climate Scale and the Modified multiple-item scale for consumer perceptions of health care service quality. RESULTS: Significant positive correlations were found among nurses' perception towards patient safety and health care service quality. The experience of nurses as members of the committee on patient safety and their employment position did not show any significant difference in the perception towards patient safety and health care services quality. Perceptions of health care service quality were lower among those with 6- to 10-year experience than with over 21 years. CONCLUSION: In the perception of nurses and nurse managers' continuous improvement, perceptions towards patient safety were related to reliability, assurance, responsiveness and empathy in health care service quality. IMPLICATIONS FOR NURSING MANAGEMENT: Generalist nurses with 21 years or more experiences in multiple departments showed high perception towards health care service quality. Experienced nurses' perceptions of activities to improve patient safety and quality of health care services are important.

Perceptions of nurse managers and staff nurses regarding Technological Competency as Caring in Nursing theory in general hospitals in Japan.

Background: Nurses as primary healthcare providers demonstrate quality nursing care through competencies with healthcare technologies, while nurse managers assume the primacy of managing quality healthcare in their respective care settings. However, little is known about perceptions of the influence of care technologies on their nursing practice. Objective: This study aimed to determine managers' and staff nurses' perceptions regarding the Technological Competency as Caring in Nursing (TCCN) theory in general hospitals in Japan. Methods: This study employed a cross-sectional survey design, with 421 participants selected using a stratified sampling method. Technological Competency as Caring in Nursing Instrument-Revised (TCCNI-R) was used for online data collection using Survey Monkey©. Data were analyzed using Welch's t-test and ANOVA. Results: Nurses with years of experience within the range of 20 to less than 30 years showed the highest TCCNI-R scores among the two groups. Nurses who had received education on caring in nursing showed significant differences for Factor 2 (Technological Competency as Caring), that of expressing Technological Competency as Caring. Three other factors showed no significant difference, namely in Factor 1 (Nursing Expression as Caring), Factor 3 (Technology and Caring), and Factor 4 (Technological Knowing). However, the average scores of these factors were high, which reflect high professional ethics and occupational discipline and increased awareness of caring in nursing. It was also found that the nurse managers were more aware of the TCCN than were the staff nurses. The nurse managers were also more aware of providing care using technology, recognizing the need-to-know patient needs through technology and providing care to the ever-changing patient's condition. Conclusion: The study discovered that continuing education is needed regarding the practice of nursing based on theory, enabling appropriate and accurate understanding of practicing knowing persons as caring in nursing.

Criterion-related Validity of the Perceived Inventory of Technological Competency as Caring in Nursing (PITCCN) in Acute Care Settings.

The Perceived Inventory of Technological Competency as Caring in Nursing (PITCCN) based on Locsin's theory of Technological Competency as Caring in Nursing (TCCN, 2005) was developed (Kato et al. 2016) and revised by Miyamoto et al. (2017). The purpose of this study was to verify criterion-related validity of PITCCN using the Practice of Caring Behavior Questionnaire (PCBQ, Shigehisa, 2007). The study was approved by the Ethical Review Board of Tokushima University Hospital. Data were collected from September 2017 to October 2017. Of the 402 questionnaire copies distributed, only 299 copies were returned and analyzed to determine correlations between total score value and Mean Factor Point of each factor for PITCCN and PCBQ. These were analyzed using Pearson correlation coefficients. Statistical significance was at .01 levels. Strong positive correlation coefficient was obtained between total score values (r=.76, p?.01) and factors of PITCCN and PCBQ (r=.19 to .68, p?.01) , except the PITCCN's factor 3 : "Utilization of information obtained from technology and continuous knowing" and PCBQ's factor 6 : "Enriched relationships between patient and nurse", (r=.13, not significant). From these results, the criterion-related validity of PITCCN as the inventory of TCCN could be confirmed. J. Med. Invest. 66 : 42-45, February, 2019.

The role of nurse administrators and managers in quality psychiatric care.

Psychiatric healthcare services in Japan demand continuous improvement to ensure quality care. Psychiatric nurse administrators and managers greatly influence the quality of services. To improve treatment, the following are considered necessary: clarification of service goal, proper assessment of treatment outcome, shortening of hospitalization, patient-centered care, establishment of trust relationships based on open communication with patients, and effective interdisciplinary teamwork. Additionally, administrators and managers must evaluate the clinical competence of individual nurses and appropriately assign them, especially when personnel shortage is an issue. Furthermore, in collaboration with other healthcare professionals, nurse managers must provide optimal care by setting goals of psychiatric services for patients in acute, sub-acute, or convalescent phases. This article presents the roles of nurse administrators and managers in improving the quality of Japanese psychiatric healthcare services.