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Record W4367672828 · doi:10.22533/at.ed.1593322328045

What was the community support offered to people with disabilities during the COVID-19 pandemic in Brazil? A pilot study as part of a global survey.

2023· article· en· W4367672828 on OpenAlexafffund
Beatriz Helena Brugnaro, Fabiana Nascimento Vieira, Olaf Kraus de Camargo, Nelci Adriana Cicuto Ferreira Rocha

Bibliographic record

VenueInternational Journal of Health Science · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth, Nursing, Elderly Care
Canadian institutionsMcMaster University
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de São PauloMcMaster University
KeywordsCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)GeographyPsychologyMedicineVirologyDiseaseInfectious disease (medical specialty)Outbreak

Abstract

fetched live from OpenAlex

Background: The COVID 19 pandemic has imposed challenges on the world, requiring strict biosecurity measures.Thus, community supports are of great importance for coping with the pandemic for people with disabilities.We aimed to identify and analyze community supports for people with disabilities during the pandemic in Brazil.Methods: Participants with disabilities or related to people with disabilities, including professionals of health care, answered an electronic form about the community supports offered during the pandemic, using categorical data.Results: Participated 105 individuals.It was found that the majority reported that people with disabilities did not have available accessible screening for COVID-19, nor had facilities accessible for quarantine or updates about the pandemic.Further on, did not have home care from personal support workers, home care nurses or caregivers, provision of personal protective equipment, nor online medical care, online educational support, personal technical equipment or delivery services available.Conclusions: The COVID-19 pandemic further exacerbated the dire situation to access health and community services in Brazil.The findings highlight and give more visibility to the great demand that this population still has in Brazil and enables targeted and effective social actions for the population with disabilities during and after pandemic period, enabling better health and social care for them, not only for Brazil, but also for other developing countries.

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)0.030
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0300.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.244
GPT teacher head0.534
Teacher spread0.290 · 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.

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

Citations1
Published2023
Admission routes2
Has abstractyes

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