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Record W3176629812 · doi:10.3390/su13137107

Impact of the COVID-19 Pandemic on Family Carers of Older People Living with Dementia in Italy and Hungary

2021· article· en· W3176629812 on OpenAlexaboutno aff
László Kostyál, Zsuzsa Széman, Virág Erzsébet Almási, Paolo Fabbietti, Sabrina Quattrini, Marco Socci, Giovanni Lamura, Cristina Gagliardi

Bibliographic record

VenueSustainability · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaPandemicGovernment (linguistics)Quarter (Canadian coin)Coronavirus disease 2019 (COVID-19)GerontologyPsychologyMedicineFamily memberEconomic growthGeographyDiseaseFamily medicineEconomics

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has had a major effect on both older people with dementia and families caring for them. This paper presents the results of an online survey carried out among Italian and Hungarian family carers of people with dementia during the first pandemic wave (May–July 2020, n = 370). The research questions were the following: (1) How has the pandemic changed the lives of family carers? (2) How did government restriction measures change the availability of care-related help? (3) What other changes did families experience? Results show that about one-quarter of both subsamples experienced a deterioration in their financial status. A decline in both general and mental health was also reported. Due to “lockdown”, family carers’ burden increased substantially. Utilization of care-related help decreased, and the share of those left with no help increased in both countries. Cross-country differences emerged in terms of dementia care system, severity of the first pandemic wave, and measures put in place by governments. Findings outline the weaknesses of support structures and their country-specific vulnerabilities to a worldwide pandemic. To better protect people with dementia in the future, it is essential to strengthen their family carers, and support structures need to be re-evaluated and re-designed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.029
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.310
Teacher spread0.298 · 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 teacher head, 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

Citations21
Published2021
Admission routes1
Has abstractyes

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