&lt;p&gt;Frustrated Caring: Family Members’ Experience of Motivating COPD Patients Towards Self-Management&lt;/p&gt;

Sigurgeirsdóttir, Jónína; Halldórsdóttir, Sigríður; Arnardóttir, Ragnheiður Harpa; Guðmundsson, Gunnar; Björnsson, Eyþór

doi:10.2147/copd.s273903

Cited by 10 publications

(10 citation statements)

References 30 publications

(55 reference statements)

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“…These findings can be explained by the intergenerational effects of the parent's health status on depression in children. Children may feel stressed, powerless, and depressed in the presence of their mother's depression and fatigue leading to cough and breathlessness ( 42 ).…”

Section: Discussionmentioning

confidence: 99%

Machine learning models for predicting risk of depression in Korean college students: Identifying family and individual factors

Gil

Kim

Min

2022

Front. Public Health

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BackgroundDepression is one of the most prevalent mental illnesses among college students worldwide. Using the family triad dataset, this study investigated machine learning (ML) models to predict the risk of depression in college students and identify important family and individual factors.MethodsThis study predicted college students at risk of depression and identified significant family and individual factors in 171 family data (171 fathers, mothers, and college students). The prediction accuracy of three ML models, sparse logistic regression (SLR), support vector machine (SVM), and random forest (RF), was compared.ResultsThe three ML models showed excellent prediction capabilities. The RF model showed the best performance. It revealed five significant factors responsible for depression: self-perceived mental health of college students, neuroticism, fearful-avoidant attachment, family cohesion, and mother's depression. Additionally, the logistic regression model identified five factors responsible for depression: the severity of cancer in the father, the severity of respiratory diseases in the mother, the self-perceived mental health of college students, conscientiousness, and neuroticism.DiscussionThese findings demonstrated the ability of ML models to accurately predict the risk of depression and identify family and individual factors related to depression among Korean college students. With recent developments and ML applications, our study can improve intelligent mental healthcare systems to detect early depressive symptoms and increase access to mental health services.

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Section: Discussionmentioning

confidence: 99%

Machine learning models for predicting risk of depression in Korean college students: Identifying family and individual factors

Gil

Kim

Min

2022

Front. Public Health

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“…Future research might include a greater mix of employment status, carer experience and cultural background, and include the views of carers living in rural areas. Carer burden and the roles of carers in supporting the self-management of people with COPD has been explored [ 8 , 9 ]. Studies conducted show that carers reported health-related problems resulting from their caring role.…”

Section: Discussionmentioning

confidence: 99%

“It's like being on a roller coaster”: the burden of caring for people with severe asthma

et al. 2021

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IntroductionFamily carers and significant others play a fundamental role in the well-being of people with severe asthma. This study aimed to investigate the challenges faced by family carers/significant others of people with severe asthma, to understand if there is an unmet need and to explore coping strategies.MethodsCarers of people with severe asthma were invited to participate in a face-to-face or telephone interview. Semi-structured interviews were conducted until reaching data saturation of themes. The 20 interviews were recorded, transcribed and analysis of data followed an inductive thematic approach.ResultsWe report three overarching emergent themes:(1) “Caring role impacts”: centred around the negative and positive impacts of caring on carers' well-being; (2) “Unmet needs”: encapsulated the support needs participants desired and were categorised into unmet: informational, biopsychosocial needs, and carers' involvement in decision making; (3) “Coping strategies”: central to the range of tools and positive approaches in dealing with caring demands.DiscussionCaring for someone with severe asthma can be burdensome and may negatively affect the physical and psychosocial health of the carer. Various coping strategies are used to manage the demands of these caring roles. Carers of people with severe asthma expressed a need for information, biopsychosocial, and involvement in care decision making support. Tailored support services that are sensitive to their needs may improve their quality of life and encourage healthcare providers to value and acknowledge the important contribution that carers make.

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“…47 It might be helpful, as previously suggested, to identify what kind of education would be most effective in each phase of the progressive and exacerbating COPD, early stage, intermediary stage, or advanced stage. 28 Our results also imply that GPs express a need for education about available resources for COPD patients. All participants were aware of the importance of patients' motivation regarding COPD treatment.…”

Section: Dovepressmentioning

confidence: 63%

“…27 Furthermore, we have found that COPD patients' immediate families (spouse/adult children) experience frustration against an advancing burden of caring and trying to motivate the patients towards self-management. 28 Self-management interventions in COPD focus on individuals' needs, aim at improving their self-management skills, giving feedback on new skills, focusing on exacerbations rate, duration of hospitalizations and healthcare costs, enhancing patients' active role and improved health-related quality of life (HRQoL). 15,29 Healthcare professionals receive little guidance in providing selfmanagement support, and the available resources have been outside the scope of traditional training.…”

Section: Introductionmentioning

confidence: 99%

Ethical Dilemmas in Physicians’ Consultations with COPD Patients

Sigurgeirsdóttir¹,

Halldórsdóttir²,

Arnardóttir³

et al. 2022

COPD

Self Cite

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This phenomenological study was aimed at exploring principal physicians' (participants') experience of attending to COPD patients and motivating their self-management, in light of the GOLD clinical guidelines of COPD therapy. Methods: Interviews were conducted with nine physicians, who had referred patients to PR, five general practitioners (GPs) and four lung specialists (LSs). The interviews were recorded, transcribed, and analyzed through a process of deconstruction and reconstruction. Results: The participants experienced several ethical dilemmas in being principal physicians of COPD patients and motivating their self-management; primarily in the balancing act of adhering to the Hippocratic Oath of promoting health and saving lives, while respecting their patients' choice regarding non-adherence eg, by still smoking. It was also a challenge to deal with COPD as a nicotine addiction disease, deal with patients' denial regarding the harm of smoking and in motivating patient mastery of the disease. The participants used various strategies to motivate their patients' self-management such as active patient education, enhancing the patients' inner motivation, by means of an interdisciplinary approach, involving the patients' significant other when appropriate, and by proposing PR. Conclusion:The findings indicate that being a principal physician of COPD patients and motivating their self-management is a balancing act, involving several dilemmas. Patients' nicotine addiction and physicians' ethical obligations are likely to create ethical dilemmas as the physician is obligated to respect the patients' will, even though it contradicts what is best for the patient. The participants suggest strategies to motivate COPD patients' self-management.

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<p>Frustrated Caring: Family Members’ Experience of Motivating COPD Patients Towards Self-Management</p>

Cited by 10 publications

References 30 publications

Machine learning models for predicting risk of depression in Korean college students: Identifying family and individual factors

Machine learning models for predicting risk of depression in Korean college students: Identifying family and individual factors

“It's like being on a roller coaster”: the burden of caring for people with severe asthma

Ethical Dilemmas in Physicians’ Consultations with COPD Patients

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