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Record W3133004357 · doi:10.1080/17538157.2021.1883029

Awareness of the Egyptian public about COVID-19: what we do and do not know

2021· article· en· W3133004357 on OpenAlexaboutno aff
Nirmeen A. Sabry, Seif El Hadidi, Ahmed Kamel, Maggie Abbassi, Samar Farid

Bibliographic record

VenueInformatics for Health and Social Care · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicRespondentCoronavirus disease 2019 (COVID-19)Quarter (Canadian coin)HotlinePopulationMedicinePersonal protective equipmentPublic healthFamily medicineHealth careSocial distanceDemographicsSocial mediaEnvironmental healthDemographyGeographyNursingDiseaseSociology

Abstract

fetched live from OpenAlex

To survey the health-seeking behaviors and perspectives of the Egyptian population toward the COVID-19 pandemic. A descriptive survey was designed and disseminated via social media platforms. The survey consisted of 32 questions addressing respondent's demographics, knowledge, practice, and attitude toward the COVID-19 pandemic. A total of 25,994 Egyptians participated in the survey from the 29 Egyptian governorates. More than 99% of the respondents were aware of the COVID-19 pandemic. Responses showed split opinions regarding whether people should wear gloves or masks to prevent COVID-19 infection (47.7% and 49.5% replied with "False", respectively). Almost one-quarter (23.1%) of the respondents went to crowded places during the last 14 days. Calling the emergency hotline and self-isolation at home were the most frequent practices to deal with COVID-19 symptoms (34.1% and 44.5%, respectively). A total of 85% of respondents reported their confidence in the Egyptian healthcare system to win the battle against COVID-19 despite the challenges. A vast majority of this large population sample reported reasonable knowledge levels and potentially appropriate practices toward COVID-19.

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)0.000
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 score0.672
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.088
GPT teacher head0.447
Teacher spread0.359 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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