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Record W3109303019 · doi:10.5430/jnep.v11n3p47

Prevention measures against COVID-19 performed by people with diabetes mellitus

2020· article· en· W3109303019 on OpenAlexvenueno aff
Shérida Karanini Paz de Oliveira, Amelina de Brito Belchior, Rhanna Emanuela Fontenele de Lima Carvalho, Natércia Brígido Linhares Fernandes, Rebeca Furtado Fernandes, Natasha Albuquerque Vasconcelos, Patrícia Freire de Vasconcelos

Bibliographic record

VenueJournal of Nursing Education and Practice · 2020
Typearticle
Languageen
FieldComputer Science
TopicHealthcare during COVID-19 Pandemic
Canadian institutionsnot available
Fundersnot available
KeywordsDiabetes mellitusGlycated hemoglobinMedicineCoronavirus disease 2019 (COVID-19)Environmental healthHand washingType 2 diabetesSocial distanceDescriptive statisticsHygieneDiabetes managementGerontologyFamily medicineInternal medicineDiseaseInfectious disease (medical specialty)Endocrinology

Abstract

fetched live from OpenAlex

In the absence of treatment and vaccine against coronavirus, preventive measures must be adopted, especially by people with diabetes. The objective was to identify the preventive measures against the new coronavirus carried out by people with diabetes. This is a cross-sectional study carried out with 214 people with diabetes through a questionnaire provided by a Google forms link in June 2020. We used descriptive statistics and the Spearman test to compare means. Most participants were female (85.9%), young adults (84.1%), with type 1 diabetes (76.7%), and without complications (74.2%). A high average of glycated hemoglobin (7.5 ± 1.4), changes in selfcare routine (72%) and in blood glucose levels (72.8%), wearing masks (98.1%), washing hands with soap and water (96.7%), using alcohol gel (94.4%), and social distancing (85%) were observed. It is essential to reinforce care for the metabolic control of people with diabetes, in addition to optimizing strategies against the COVID-19 epidemic with a view to preventing and promoting health.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.883
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.089
GPT teacher head0.406
Teacher spread0.317 · 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 designNot applicable
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
Published2020
Admission routes1
Has abstractyes

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