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Record W4387499123 · doi:10.2196/47510

Sociodemographic Profile, Health Conditions, and Burden of Informal Caregivers of Older Adults in Brazil During the COVID-19 Pandemic: Cross-Sectional, Exploratory, Noninterventional, Descriptive Study

2023· article· en· W4387499123 on OpenAlexvenueno aff
Julielen Larissa Alexandrino Moraes, Mateus Cunha Gomes, Lucianne Nascimento de Araujo, Tainá Sayuri Onuma de Oliveira, Glenda Roberta Oliveira Naiff Ferreira, Cintia Yolette Urbano Pauxis Aben‐Athar, Sílvio Éder Dias da Silva, Aline Maria Pereira Cruz Ramos, Diego Pereira Rodrigues, Fabianne de Jesus Dias de Sousa

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsnot available
FundersPró-Reitoria de Pesquisa e Pós-Graduação, Universidade Federal do ParáUniversidade Federal do Pará
KeywordsPandemicContext (archaeology)Descriptive statisticsCross-sectional studyGerontologyQuality of life (healthcare)Descriptive researchCaregiver burdenMedicinePopulationPsychologyCoronavirus disease 2019 (COVID-19)DiseaseNursingEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Demographic changes in the world population have resulted in an increasingly aging society, with a progressive increase in the number of people in situations of dependence, who require assistance from family members to meet their basic needs. Caring for older adults involves performing diverse activities, resulting in reduced free time and tiredness, and fulfilling the demands and expectations related to personal, family, physical, and social life, consequently compromising the quality of life of the caregiver. In this context, the informal caregiver of hospitalized older adults emerges as the focus of attention. OBJECTIVE: The aim of this study was to describe the sociodemographic profile, health conditions, and burden of informal caregivers of older adults admitted to a university hospital in Brazil during the COVID-19 pandemic period. METHODS: This is a cross-sectional, descriptive, and analytical study that was conducted with 25 informal caregivers of hospitalized older adults in a university hospital in Brazil between August and September 2022. Three instruments were applied: Caregiver Burden Inventory, sociodemographic questionnaire, and health conditions questionnaire. The data were analyzed using SPSS version 28.0. Descriptive (frequency and percentage) and inferential analyses were performed using 2-sided Student t test with 95% CIs. RESULTS: Of the 25 interviewees, 18 (72%) were females, 17 (46%) were married or in a stable union, 14 (56%) completed secondary education, and 11 (44%) lived with the older adults who needed care. The average age of the participants was 44 (SD 12.8) years. Regarding their health conditions, most caregivers self-reported it as good (12/25, 48%). They provided care to their father or mother older than 70 years (14/25, 56%). The Caregiver Burden Inventory analysis showed that the caregivers were the most negatively impacted in the domains of personal life overload (mean 10.8, SD 3.46; P=.047) and physical overload (mean 10.6, SD 2.32; P=.02). CONCLUSIONS: In recent years, there has been an increase in the burden on informal caregivers of hospitalized older adults in Brazil, thereby impacting their personal and physical lives. The findings of our study show that health care professionals should be trained to promote health guidelines and actions to improve the personal and physical lives of the caregiver population in Brazil.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.208
GPT teacher head0.515
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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