The European policy emphasis on providing informal care at home causes caregivers and home care professionals having more contact with each other, which makes it important for them to find satisfying ways to share care. Findings from the literature show that sharing care between caregivers and professionals can be improved. This study therefore examines to what degree and why caregivers’ judgements on sharing care with home care professionals vary. To improve our understanding of social inequities in caregiving experiences, the study adopts an intersectional perspective. We investigate how personal and situational characteristics attached to care judgements are interwoven. Using data of the Netherlands Institute for Social Research, we conducted bivariate and multivariate linear regression analysis (N = 292). We combined four survey questions into a 1–4 scale on ‘caregiver judgement’ (α = 0.69) and used caregivers’ personal (such as gender and health status) and situational characteristics (such as the care recipient's impairment and type of care) as determinants to discern whether these are related to the caregivers’ judgement. Using a multiplicative approach, we also examined the relationship between mutually constituting factors of the caregivers’ judgement. Adjusted for all characteristics, caregivers who provide care to a parent or child with a mental impairment and those aged between 45 and 64 years or with a paid job providing care to someone with a mental impairment are likely to judge sharing care more negatively. Also, men providing care with help from other caregivers and caregivers providing care because they like to do so who provide domestic help seem more likely to be less satisfied about sharing care. This knowledge is vital for professionals providing home care, because it clarifies differences in caregivers’ experiences and hence induce knowledge how to pay special attention to those who may experience less satisfaction while sharing care.
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How can Zuyd University promote knowledge sharing between different departments and locations, and what structures are needed to enable the knowledge sharing?
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Background: The substitution of healthcare is a way to control rising healthcare costs. The Primary Care Plus (PC+) intervention of the Dutch ‘Blue Care’ pioneer site aims to achieve this feat by facilitating consultations with medical specialists in the primary care setting. One of the specialties involved is dermatology. This study explores referral decisions following dermatology care in PC+ and the influence of predictive patient and consultation characteristics on this decision. Methods: This retrospective study used clinical data of patients who received dermatology care in PC+ between January 2015 and March 2017. The referral decision following PC+, (i.e., referral back to the general practitioner (GP) or referral to outpatient hospital care) was the primary outcome. Stepwise logistic regression modelling was used to describe variations in the referral decisions following PC+, with patient age and gender, number of PC+ consultations, patient diagnosis and treatment specialist as the predicting factors. Results: A total of 2952 patients visited PC+ for dermatology care. Of those patients with a registered referral, 80.2% (N = 2254) were referred back to the GP, and 19.8% (N = 558) were referred to outpatient hospital care. In the multivariable model, only the treating specialist and patient’s diagnosis independently influenced the referral decisions following PC+. Conclusion: The aim of PC+ is to reduce the number of referrals to outpatient hospital care. According to the results, the treating specialist and patient diagnosis influence referral decisions. Therefore, the results of this study can be used to discuss and improve specialist and patient profiles for PC+ to further optimise the effectiveness of the initiative.
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The aim of the ProInCa project was to develop the sustainable innovation capacity of Kazakhstan’s Medical Universities for the modernization of nursing. The project was coordinated by JAMK University of Applied Sciences and consisted of a consortium of five Kazakhstani medical universities and four European higher education institutions. The project was co-funded by the Erasmus+ Capacity Building in the Field of Higher Education programme and supported by the Ministry of Education and Science and the Ministry of Healthcare of the Republic of Kazakhstan during 15.10.2017 – 31.01.2021.he wider objective of the ProInCa project is to develop the sustainable innovation capacity of Kazakhstan’s Medical Universities for the modernization of nursing. This wider objective is divided into four specific objectives, which are:1. Development of mechanisms for collaboration and knowledge sharing between academic national and international nursing community and society.2. To learn from best practices on implementing evidence-based nursing in nursing research, education and practice to promote the efficiency and quality of health care.3. Strengthen higher education institutes’ role in building evidence-based nursing research activities in health services to promote quality and safety of health care system.4. Promote the capacity and system of nursing leadership and management in health care transition to improve the quality of health care system
The modern economy is largely data-driven and relies on the processing and sharing of data across organizations as a key contributor to its success. At the same time, the value, amount, and sensitivity of processed data is steadily increasing, making it a major target of cyber-attacks. A large fraction of the many reported data breaches happened in the healthcare sector, mostly affecting privacy-sensitive data such as medical records and other patient data. This puts data security technologies as a priority item on the agenda of many healthcare organizations, such as of the Dutch health insurance company Centraal Ziekenfonds (CZ). In particular when it comes to sharing data securely, practical data protection technologies are lacking as they mostly focus on securing the link between two organizations while being completely oblivious of what is happening with the data after sharing. For CZ, searchable encryption (SE) technologies that allow to share data in encrypted form, while enabling the private search on this encrypted data without the need to decrypt, are of particular interest. Unfortunately, existing efficient SE schemes completely leak the access pattern (= pattern of encrypted search results, e.g. identifiers of retrieved items) and the search pattern (= pattern of search queries, e.g. frequency of same queries), making them susceptible to leakage-abuse attacks that exploit this leakage to recover what has been queried for and/or (parts of) the shared data itself. The SHARE project will investigate ways to reduce the leakage in searchable encryption in order to mitigate the impact of leakage-abuse attacks while keeping the performance-level high enough for practical use. Concretely, we propose the construction of SE schemes that allow the leakage to be modeled as a statistic released on the queries and shared dataset in terms of ε-differential privacy, a well-established notion that informally says that, after observing the statistic, you learn approximately (determined by the ε-parameter) the same amount of information about an individual data item or query as if the item was not present in the dataset or the query has not been performed. Naturally, such an approach will produce false positives and negatives in the querying process, affecting the scheme’s performance. By calibrating the ε-parameter, we can achieve various leakage-performance trade-offs tailored to the needs of specific applications. SHARE will explore the idea of differentially-private leakage on different parts of SE with different search capabilities, starting with exact-keyword-match SE schemes with differentially-private leakage on the access pattern only, up to schemes with differentially-private leakage on the access and search pattern as well as on the shared dataset itself, allowing for more expressive query types like fuzzy match, range, or substring queries. SHARE comes with an attack lab in which we investigate existing and new types of leakage-abuse attacks to assess the mitigation-potential of our proposed combination of differential privacy with cryptographic guarantees in searchable encryption. To stimulate commercial exploitation of SHARE-results, our consortium partners CZ and TNO will take the lead on applying and evaluating our envisioned technologies in various healthcare use-cases.
