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Record W4400955603 · doi:10.3233/shti240147

Family Care in Members with COVID-19 Using Telenursing

2024· article· en· W4400955603 on OpenAlexaff
Maricarmen Moreno Tochihuitl, Carmen Cruz-Rivera, Rayo Cruz-Rivera, María del Rosario Ricardez-Ramírez, Lizamar Lorena Ramos-Sanchez, Lorraine J. Block

Bibliographic record

VenueStudies in health technology and informatics · 2024
Typearticle
Languageen
FieldComputer Science
TopicHealthcare during COVID-19 Pandemic
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicMedicineHealth careTelemedicineHygiene2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)TelehealthFamily medicineMedical emergencyNursingDiseaseInfectious disease (medical specialty)VirologyPolitical science

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has caused the infection and death of millions of people around the world. The Pan American Health Organization (PAHO) and the Center for Disease Control and Prevention have issued recommendations for caregivers of patients sent home with COVID-19, such as; the use of facemasks, hygiene at home, the use of the vaccine, among others. The purpose of the study was to determine the relationship between the level of support from information technologies (Whatsapp) with the level of knowledge to provide better care at home by family caregivers of people with COVID-19 by an educational program to 130 caregivers.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.447
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.083
GPT teacher head0.422
Teacher spread0.339 · 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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

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