从个体特征预测家庭照顾者负担。

IF 2.5 4区 医学 Q2 HEALTH CARE SCIENCES & SERVICES Informatics for Health & Social Care Pub Date : 2022-04-03 Epub Date: 2021-10-28 DOI:10.1080/17538157.2021.1988955
Mike K P So, Helina Yuk, Agnes Tiwari, Sam T Y Cheung, Amanda M Y Chu
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引用次数: 3

摘要

本研究考察了照顾者负担与其个体特征之间的关系,并确定了有助于预测照顾者负担水平的特征。我们成功地调查了387名家庭照顾者,让他们完成照顾者负担量表(CBI)和个人特征问卷。当我们比较具有特定个体特征(包括照顾老年人、教育水平、就业状况、出生地、婚姻状况、经济状况、对家庭支持的需求、对朋友支持的需求和对非营利组织支持的需求)的群体之间的平均CBI得分时,我们发现平均得分存在显著差异。通过logistic回归模型,以负担水平为结果,我们发现照顾老年人、教育水平、就业状况、出生地、经济状况和对非营利组织支持的需求是照顾者负担水平的显著预测因子。研究结果表明,可以采用某些个体特征来识别和量化可能具有较高负担水平的照顾者。这些发现有助于发现可能需要及时支持和社会关怀的护理人员。
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Predicting the burden of family caregivers from their individual characteristics.

This study examined the association between caregivers' burdens and their individual characteristics and identified characteristics that are useful for predicting the level of caregiver burden. We successfully surveyed 387 family caregivers, having them complete the caregiver burden inventory scale (CBI) and an individual characteristic questionnaire. When we compared the average CBI scores between groups with a particular individual characteristic (including caring for older adult(s), educational level, employment status, place of birth, marital status, financial status, need for family support, need for friend support, and need for nonprofit organizational support), we found a significant difference in the average scores. From a logistic regression model, with burden level as the outcome, we found that caring for older adult(s), educational level, employment status, place of birth, financial situation, and need for nonprofit organizational support were significant predictors of the burden level of caregivers. The research findings suggest that certain individual characteristics can be adopted for identifying and quantifying caregivers who may have a higher level of burden. The findings are useful to uncover caregivers who may need prompt support and social care.

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来源期刊
CiteScore
6.10
自引率
4.20%
发文量
21
审稿时长
>12 weeks
期刊介绍: Informatics for Health & Social Care promotes evidence-based informatics as applied to the domain of health and social care. It showcases informatics research and practice within the many and diverse contexts of care; it takes personal information, both its direct and indirect use, as its central focus. The scope of the Journal is broad, encompassing both the properties of care information and the life-cycle of associated information systems. Consideration of the properties of care information will necessarily include the data itself, its representation, structure, and associated processes, as well as the context of its use, highlighting the related communication, computational, cognitive, social and ethical aspects. Consideration of the life-cycle of care information systems includes full range from requirements, specifications, theoretical models and conceptual design through to sustainable implementations, and the valuation of impacts. Empirical evidence experiences related to implementation are particularly welcome. Informatics in Health & Social Care seeks to consolidate and add to the core knowledge within the disciplines of Health and Social Care Informatics. The Journal therefore welcomes scientific papers, case studies and literature reviews. Examples of novel approaches are particularly welcome. Articles might, for example, show how care data is collected and transformed into useful and usable information, how informatics research is translated into practice, how specific results can be generalised, or perhaps provide case studies that facilitate learning from experience.
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