PURPOSE: The Nasality Severity Index 2.0 (NSI 2.0) forms a new, multiparametric approach in the assessment of hypernasality. To enable clinical implementation of this index, the short- and long-term test-retest reliability of this index was explored. METHODS: In 40 normal-speaking adults (mean age 32y, SD 11, 18-56y) and 29 normal-speaking children (mean age 8y, SD 2, 4-12y), the acoustic parameters included in the NSI 2.0 (i.e. nasalance of the vowel /u/ and an oral text, and the voice low tone to high tone ratio (VLHR) of the vowel /i/) were obtained twice at the same test moment and during a second assessment two weeks later. After determination of the NSI 2.0, a comprehensive set of statistical measures was applied to determine its reliability. RESULTS: Long-term variability of the NSI 2.0 and its parameters was slightly higher compared to the short-term variability, both in adults and in children. Overall, a difference of 2.82 for adults and 2.68 for children between the results of two consecutive measurements can be interpreted as a genuine change. With an ICC of 0.84 in adults and 0.77 in children, the NSI 2.0 additionally shows an excellent relative consistency. No statistically significant difference was withheld in the reliability of test-retest measurements between adults and children. CONCLUSION: Reliable test-retest measurements of the NSI 2.0 can be performed. Consequently, the NSI 2.0 can be applied in clinical practice, in which successive NSI 2.0 scores can be reliably compared and interpreted. LEARNING OUTCOMES: The reader will be able to describe and discuss both the short-term and long-term test-retest reliability of the Nasality Severity Index 2.0, a new multiparametric approach to hypernasality, and its parameters. Based on this information, the NSI 2.0 can be applied in clinical practice, in which successive NSI 2.0 scores, e.g. before and after surgery or speech therapy, can be compared and interpreted.
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BACKGROUND: Recent evidence suggests that an increase in baccalaureate-educated registered nurses (BRNs) leads to better quality of care in hospitals. For geriatric long-term care facilities such as nursing homes, this relationship is less clear. Most studies assessing the relationship between nurse staffing and quality of care in long-term care facilities are US-based, and only a few have focused on the unique contribution of registered nurses. In this study, we focus on BRNs, as they are expected to serve as role models and change agents, while little is known about their unique contribution to quality of care in long-term care facilities. METHODS: We conducted a cross-sectional study among 282 wards and 6,145 residents from 95 Dutch long-term care facilities. The relationship between the presence of BRNs in wards and quality of care was assessed, controlling for background characteristics, i.e. ward size, and residents' age, gender, length of stay, comorbidities, and care dependency status. Multilevel logistic regression analyses, using a generalized estimating equation approach, were performed. RESULTS: 57% of the wards employed BRNs. In these wards, the BRNs delivered on average 4.8 min of care per resident per day. Among residents living in somatic wards that employed BRNs, the probability of experiencing a fall (odds ratio 1.44; 95% CI 1.06-1.96) and receiving antipsychotic drugs (odds ratio 2.15; 95% CI 1.66-2.78) was higher, whereas the probability of having an indwelling urinary catheter was lower (odds ratio 0.70; 95% CI 0.53-0.91). Among residents living in psychogeriatric wards that employed BRNs, the probability of experiencing a medication incident was lower (odds ratio 0.68; 95% CI 0.49-0.95). For residents from both ward types, the probability of suffering from nosocomial pressure ulcers did not significantly differ for residents in wards employing BRNs. CONCLUSIONS: In wards that employed BRNs, their mean amount of time spent per resident was low, while quality of care on most wards was acceptable. No consistent evidence was found for a relationship between the presence of BRNs in wards and quality of care outcomes, controlling for background characteristics. Future studies should consider the mediating and moderating role of staffing-related work processes and ward environment characteristics on quality of care.
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BACKGROUND: Limited research has examined what is actually done in the process of care by nursing staff in long-term institutional care. The applied instruments employed different terminologies, and psychometric properties were inadequately described. This study aimed to develop and test an observational instrument to identify and examine the amount of time spent on nursing interventions in long-term institutional care using a standardized language.METHODS: The Groningen Observational instrument for Long-Term Institutional Care (GO-LTIC) is based on the conceptual framework of the Nursing Interventions Classification. Developmental, validation, and reliability stages of the GO-LTIC included: 1) item generation to identify potential setting-specific interventions; 2) examining content validity with a Delphi panel resulting in relevant interventions by calculating the item content validity index; 3) testing feasibility with trained observers observing nursing assistants; and 4) calculating inter-rater reliability using (non) agreement and Cohen's kappa for the identification of interventions and an intraclass correlation coefficient for the amount of time spent on interventions. Bland-Altman plots were applied to visualize the agreement between observers. A one-sample student T-test verified if the difference between observers differed significantly from zero.RESULTS: The final version of the GO-LTIC comprised 116 nursing interventions categorized into six domains. Substantial to almost perfect kappa's were found for interventions in the domains basic (0.67-0.92) and complex (0.70-0.94) physiological care. For the domains of behavioral, family, and health system interventions, the kappa's ranged from fair to almost perfect (0.30-1.00). Intraclass correlation coefficients for the amount of time spent on interventions ranged from fair to excellent for the physiological domains (0.48-0.99) and poor to excellent for the other domains (0.00-1.00). Bland Altman plots indicated that the clinical magnitude of differences in minutes was small. No statistical significant differences between observers (p > 0.05) were found.CONCLUSIONS: The GO-LTIC shows good content validity and acceptable inter-rater reliability to examine the amount of time spent on nursing interventions by nursing staff. This may provide managers with valuable information to make decisions about resource allocation, task allocation of nursing staff, and the examination of the costs of nursing services.
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Background Clients facing decision-making for long-term care are in need of support and accessible information. Construction of preferences, including context and calculations, for clients in long-term care is challenging because of the variability in supply and demand. This study considers clients in four different sectors of long-term care: the nursing and care of the elderly, mental health care, care of people with disabilities, and social care. The aim is to understand the construction of preferences in real-life situations. Method Client choices were investigated by qualitative descriptive research. Data were collected from 16 in-depth interviews and 79 client records. Interviews were conducted with clients and relatives or informal caregivers from different care sectors. The original client records were explored, containing texts, letters, and comments of clients and caregivers. All data were analyzed using thematic analysis. Results Four cases showed how preferences were constructed during the decision-making process. Clients discussed a wide range of challenging aspects that have an impact on the construction of preferences, e.g. previous experiences, current treatment or family situation. This study describes two main characteristics of the construction of preferences: context and calculation. Conclusion Clients face diverse challenges during the decision-making process on long-term care and their construction of preferences is variable. A well-designed tool to support the elicitation of preferences seems beneficial.
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Purpose: Collaborative deliberation comprises personal engagement, recognition of alternative actions, comparative learning, preference elicitation, and preference integration. Collaborative deliberation may be improved by assisting preference elicitation during shared decision-making. This study proposes a framework for preference elicitation to facilitate collaborative deliberation in long-term care consultations. Methods: First, a literature overview was conducted comprising current models for the elicitation of preferences in health and social care settings. The models were reviewed and compared. Second, qualitative research was applied to explore those issues that matter most to clients in long-term care. Data were collected from clients in long-term care, comprising 16 interviews, 3 focus groups, 79 client records, and 200 online client reports. The qualitative analysis followed a deductive approach. The results of the literature overview and qualitative research were combined. Results: Based on the literature overview, five overarching domains of preferences were described: “Health”, “Daily life”, “Family and friends”, ”Living conditions”, and “Finances”. The credibility of these domains was confirmed by qualitative data analysis. During interviews, clients addressed issues that matter in their lives, including a “click” with their care professional, safety, contact with loved ones, and assistance with daily structure and activities. These data were used to determine the content of the domains. Conclusion: A framework for preference elicitation in long-term care is proposed. This framework could be useful for clients and professionals in preference elicitation during collaborative deliberation.
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As more and more older adults prefer to stay in their homes as they age, there’s a need for technology to support this. A relevant technology is Artificial Intelligence (AI)-driven lifestyle monitoring, utilizing data from sensors placed in the home. This technology is not intended to replace nurses but to serve as a support tool. Understanding the specific competencies that nurses require to effectively use it is crucial. The aim of this study is to identify the essential competencies nurses require to work with AI-driven lifestyle monitoring in longterm care. Methods: A three round modified Delphi study was conducted, consisting of two online questionnaires and one focus group. A group of 48 experts participated in the study: nurses, innovators, developers, researchers, managers and educators. In the first two rounds experts assessed clarity and relevance on a proposed list of competencies, with the opportunity to provide suggestions for adjustments or inclusion of new competencies. In the third round the items without consensus were bespoken in a focus group. Findings: After the first round consensus was reached on relevance and clarity on n = 46 (72 %) of the competencies, after the second round on n = 54 (83 %) of the competencies. After the third round a final list of 10 competency domains and 61 sub-competencies was finalized. The 10 competency domains are: Fundamentals of AI, Participation in AI design, Patient-centered needs assessment, Personalisation of AI to patients’ situation, Data reporting, Interpretation of AI output, Integration of AI output into clinical practice, Communication about AI use, Implementation of AI and Evaluation of AI use. These competencies span from basic understanding of AIdriven lifestyle monitoring, to being able to integrate it in daily work, being able to evaluate it and communicate its use to other stakeholders, including patients and informal caregivers. Conclusion: Our study introduces a novel framework highlighting the (sub)competencies, required for nurses to work with AI-driven lifestyle monitoring in long-term care. These findings provide a foundation for developing initial educational programs and lifelong learning activities for nurses in this evolving field. Moreover, the importance that experts attach to AI competencies calls for a broader discussion about a potential shift in nursing responsibilities and tasks as healthcare becomes increasingly technologically advanced and data-driven, possibly leading to new roles within nursing.
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Background: As more and more older adults prefer to stay in their homes as they age, there’s a need for technology to support this. A relevant technology is Artificial Intelligence (AI)-driven lifestyle monitoring, utilizing data from sensors placed in the home. This technology is not intended to replace nurses but to serve as a support tool. Understanding the specific competencies that nurses require to effectively use it is crucial. The aim of this study is to identify the essential competencies nurses require to work with AI-driven lifestyle monitoring in longterm care. Methods: A three round modified Delphi study was conducted, consisting of two online questionnaires and one focus group. A group of 48 experts participated in the study: nurses, innovators, developers, researchers, managers and educators. In the first two rounds experts assessed clarity and relevance on a proposed list of competencies, with the opportunity to provide suggestions for adjustments or inclusion of new competencies. In the third round the items without consensus were bespoken in a focus group. Findings: After the first round consensus was reached on relevance and clarity on n = 46 (72 %) of the competencies, after the second round on n = 54 (83 %) of the competencies. After the third round a final list of 10 competency domains and 61 sub-competencies was finalized. The 10 competency domains are: Fundamentals of AI, Participation in AI design, Patient-centered needs assessment, Personalisation of AI to patients’ situation, Data reporting, Interpretation of AI output, Integration of AI output into clinical practice, Communication about AI use, Implementation of AI and Evaluation of AI use. These competencies span from basic understanding of AIdriven lifestyle monitoring, to being able to integrate it in daily work, being able to evaluate it and communicate its use to other stakeholders, including patients and informal caregivers. Conclusion: Our study introduces a novel framework highlighting the (sub)competencies, required for nurses to work with AI-driven lifestyle monitoring in long-term care. These findings provide a foundation for developing initial educational programs and lifelong learning activities for nurses in this evolving field. Moreover, the importance that experts attach to AI competencies calls for a broader discussion about a potential shift in nursing responsibilities and tasks as healthcare becomes increasingly technologically advanced and data-driven, possibly leading to new roles within nursing.
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For delayed and long-term students, the education process is often a lonely journey. The main conclusion of this research is that learning should not be an individual process of the student connected to one lecturer, but rather a community where learning is a collective journey. The social interaction between lecturers, groups of delayed students and other actors is an important engine for arriving at the new knowledge, insights and expertise that are important to reach their final level. This calls for the design of social structures and the collaboration mechanism that enable the bonding of all members in the community. By making use of this added value, new opportunities for the individual are created that can lead to study success. Another important conclusion is that in the design and development of learning communities, sufficient attention must be paid to cultural characteristics. Students who delay are faced with a loss of self-efficacy and feelings of shame and guilt. A learning community for delayed students requires a culture in which students can turn this experience into an experience of self-confidence, hope and optimism. This requires that the education system pays attention to language use, symbols and rituals to realise this turn. The model ‘Building blocks of a learning environment for long-term students’ contains elements that contribute to the study success of delayed and long-term students. It is the challenge for every education programme to use it in an appropriate way within its own educational context. Each department will have to explore for themselves how these elements can be translated into the actions, language, symbols and rituals that are suitable for their own target group.
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Background: During the process of decision-making for long-term care, clients are often dependent on informal support and available information about quality ratings of care services. However, clients do not take ratings into account when considering preferred care, and need assistance to understand their preferences. A tool to elicit preferences for long-term care could be beneficial. Therefore, the aim of this qualitative descriptive study is to understand the user requirements and develop a web-based preference elicitation tool for clients in need of longterm care. Methods: We applied a user-centred design in which end-users influence the development of the tool. The included end-users were clients, relatives, and healthcare professionals. Data collection took place between November 2017 and March 2018 by means of meetings with the development team consisting of four users, walkthrough interviews with 21 individual users, video-audio recordings, field notes, and observations during the use of the tool. Data were collected during three phases of iteration: Look and feel, Navigation, and Content. A deductive and inductive content analysis approach was used for data analysis. Results: The layout was considered accessible and easy during the Look and feel phase, and users asked for neutral images. Users found navigation easy, and expressed the need for concise and shorter text blocks. Users reached consensus about the categories of preferences, wished to adjust the content with propositions about well-being, and discussed linguistic difficulties. Conclusion: By incorporating the requirements of end-users, the user-centred design proved to be useful in progressing from the prototype to the finalized tool ‘What matters to me’. This tool may assist the elicitation of client’s preferences in their search for long-term care.
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Not much is known about the favourable indoor air quality in long term care facilities (LTCFs), where older adults suffering from dementia live. Older adults, especially those who suffer from dementia, are more sensible to the indoor environment. However, no special requirements for the indoor air in long term care facilities exist. Due to the decrease in cognition function, it is hard to evaluate comfort and health in this group. Nevertheless, infectious diseases are a persistent problem. Based on literature an assessment methodology has been developed to analyse LTCFs to determine if differences in building characteristics and Heating, Ventilation and Air Conditioning (HVAC) systems influence the spread of airborne infectious diseases. The developed methodology is applied in seven long term care facilities in the Netherlands. After that, the methodology has been evaluated and its feasibility and applicability are discussed. From this study, it can be concluded that this method has potential to evaluate, compare LTCFs, and develop design guidelines for these buildings. However, some adjustments to the methodology are necessary to achieve this objective. Therefore, the relation between the indoor environment and infection risk is not yet analysed, but a consistent procedure to analyse this link is provided.
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