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All Journal Nurse Media Journal of Nursing Belitung Nursing Journal Jurnal Keperawatan Soedirman (The Soedirman Journal of Nursing) Celebes Nursing Journal
Tanioka, Tetsuya
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Health Professions Nursing

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Journal : Belitung Nursing Journal

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The experience of older persons with mental health conditions who interact with healthcare robots and nurse intermediaries: The qualitative case studies Tanioka, Tetsuya; Betriana, Feni; Yokotani, Tomoya; Osaka, Kyoko; Locsin, Rozzano C.; King, Beth; Schoenhofer, Savina
Belitung Nursing Journal Vol. 7 No. 4 (2021): July - August
Publisher : Belitung Raya Foundation, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33546/bnj.1541

Abstract

Background: Caring expressions between humans and nonhuman intelligent machines are futuristic prototypes with healthcare robots as major advocates.  Objective: To examine the experience of older persons with mental health conditions, particularly patients with schizophrenia and with dementia in the interaction with healthcare robots and intermediaries in a transactive relational engagement. Methods: Two qualitative case studies were conducted using sophisticated audio-video technologies to record the conversation and activities that were carefully documented. Following the procedure for qualitative descriptive analysis, a framework based on the Transactive Relationship Theory of Nursing was employed to analyze and interpret the data.  Results: Three themes were revealed, including feelings for the other, inspiring meaningful responses, and demonstrating expressions of joy. The description of the experience of older persons involved in the conversation with humanoid robots was feeling for the other while inspiring meaningful responses in demonstrating expressions of joy.  Conclusion: This study provided initial evidence that the transactive engagements of robots with older persons with schizophrenia and dementia and nurse intermediaries in psychiatric and mental health settings can result in occasions of ‘joy’ for the patients. These findings suggest that transactive engagements with robots facilitate expressions of joy among older persons with schizophrenia and dementia. However, these findings are not intended to prescribe nursing care actions but to describe the experience of older persons who are in transactive engagements with intelligent machines, indicating the importance and value of healthcare robots in nursing older persons with schizophrenia and with dementia.
Perceptions of nurse managers and staff nurses regarding Technological Competency as Caring in Nursing theory in general hospitals in Japan Nakano, Youko; Yokotani, Tomoya; Betriana, Feni; Kawai, Chihiro; Ito, Hirokazu; Yasuhara, Yuko; Tanioka, Tetsuya; Locsin, Rozzano; Miyagawa, Misao
Belitung Nursing Journal Vol. 7 No. 6 (2021): November - December
Publisher : Belitung Raya Foundation, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33546/bnj.1767

Abstract

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.
Development of a novel instrument to measure Japanese psychiatric nurses’ technological competency as caring in nursing Takashima, Yoshiyuki; Ito, Hirokazu; Soriano, Gil P.; Yasuhara, Yuko; Osaka, Kyoko; Schoenhofer, Savina; Tanioka, Tetsuya
Belitung Nursing Journal Vol. 11 No. 1 (2025): January - February
Publisher : Belitung Raya Foundation, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33546/bnj.3623

Abstract

Background: To effectively advance person-centered care (PCC) practice, it is important to equip healthcare providers with person-centered values and beliefs while simultaneously transforming their work environment to align with PCC. Thus, instruments to measure caring practice status in nursing competency for psychiatric-specific behavioral limitations, ethico-moral behavior, technology use, and PCC need to be developed. Objective: This study developed the Technological Competency as Caring in Psychiatric Nursing Instrument (TCCNPNI) to measure practice status and test its content and construct validity. Methods: Five different phases were followed: 1) Literature Review; 2) Operational definition of the construct and development of items; 3) Two-round Delphi method; 4) Validity measure; and 5) Reliability measure. The online survey was conducted in 2024. Results: The developed instrument comprises 22 items with a 4-factor structure: competency to practice caring and person-centered care (Factor 1); competency to recognize and respond to ethical issues in psychiatry (Factor 2); competency to utilize technology in psychiatry (Factor 3); and competence to practice care for the preservation of human dignity and shared decision making (Factor 4). Cronbach’s alpha for the entire scale was 0.864, while that for factors 1-4 was 0.911, 0.814, 0.773, and 0.64, respectively. Cumulatively, these four factors contributed 49.6% and explained nearly 50% of the total data. Item-total correlation values were 0.6 or higher among factors 1-3. However, factor 4, for which items were Q30, Q33, Q34, and Q35 (r = 0.03, 0.04, 0.21, 0.11, respectively), were inverted items and had low I-T correlation values. These low correlations suggest that these items capture different concepts. The developed TCCNPNI allows for the measurement of the practice of nursing as caring in psychiatry, the state of ethico-moral behavior, and the practice status of technological competency as caring in psychiatric nursing. Conclusion: This study demonstrated satisfactorily and efficiently evaluated the practice status of technological competency in psychiatric nurses’ caring. Measuring technological competency as caring in psychiatric nursing can be an important adjunct for in-service education in psychiatric hospitals or formalized nursing education in nursing universities.
Co-Authors Betriana, Feni Blaquera, Allan Paulo Bollos, Leah Anne Christine Dino, Michael Joseph Gil, Soriano Hisaka, Yukari Ito, Hirokazu Kataoka, Mutsuko Kawai, Chihiro King, Beth Locsin, Rozzano Locsin, Rozzano C Locsin, Rozzano C. Matsumoto, Kazuyuki Miyagawa, Misao Nakano, Yoko Nakano, Youko Onishi, Kaito Osaka, Kyoko Schoenhofer, Savina Soriano, Gil Soriano, Gil P. Soriano, Krishan Takase, Kensaku Takashima, Yoshiyuki Tanioka, Ryuichi Yasuhara, Yuko Yokotani, Tomoya