Evidence map›Paper›PMID 39707200›Full record

ArticleBMC cardiovascular disorders2024

Heart failure patients' experiences of self-care neglect: a content analysis.

Parisa Sadat Bahrodi, Azade Safa, Neda Mirbagher Ajorpaz, Fatemeh Sadat Izadi Avanji

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Article in BMC cardiovascular disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Parisa Sadat BahrodiTrauma Nursing Research Center, Kashan University of Medical Sciences , Kashan, Iran.
Azade SafaTrauma Nursing Research Center, Kashan University of Medical Sciences , Kashan, Iran.
Neda Mirbagher AjorpazAutoimmune Diseases Research Center, Kashan University of Medical Sciences, Kashan, Iran. mirbagher_n@kaums.ac.ir.
Fatemeh Sadat Izadi AvanjiTrauma Nursing Research Center, Kashan University of Medical Sciences , Kashan, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHeart failure is a chronic and common disorder worldwide. Patients with heart failure need self-care behaviors to manage their condition. Despite the importance of self-care in positive health outcomes, many patients with heart failure neglect their self-care behaviors. Therefore, the present study was conducted to explain heart failure patients' experiences of self-care neglect.

methodsThis qualitative study was conducted using conventional content analysis method. Participants included 15 patients with heart failure. Data were collected through semi-structured interviews and using purposive sampling method. Sampling continued until data saturation was reached. Data analysis was performed concurrently with data collection. Lincoln and Guba's four criteria were used to ensure the trustworthiness of the data. Data management was performed using MAXQDA version 24 software.

resultsThe results were presented in the form of four main categories and 10 subcategories. In analyzing the data of the study, four main categories emerged in the participants' experiences: "false cultural beliefs in self-care", "weakening of mental-psychological power", "synergy of physical problems", and "inappropriate support".

conclusionThe patients in their experiences of neglect in self-care pointed to false cultural beliefs in self-care, weakening of mental-psychological power, synergy of physical problems, and inappropriate support. Knowing the factors that influence self-care neglect and preventing their occurrence can improve self-care skills and prevent neglect-related side effects in patients with heart failure. Healthcare providers can help improve the health of these patients by developing interventions to mitigate these factors. It is suggested that future research be designed in the form of an intervention to reduce the effect of each of these factors.

trial registrationThis is a qualitative study and has not been registered in Iranian Registry of Clinical Trials.

Indexed as

Health Knowledge, Attitudes, PracticeHeart FailureQualitative ResearchSelf CareAdultAgedCultural CharacteristicsFemaleHealth BehaviorHumansInterviews as TopicIranMaleMiddle AgedSocial SupportHeart failureNeglectQualitative content analysisSelf-care

Identifiers

PMID39707200
PMCPMC11660939

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.