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Record W4297501371 · doi:10.1371/journal.pgph.0000702

Public perception of healthcare system response to COVID-19: Findings from a web based observational study in Villavicencio, Colombia

2022· article· en· W4297501371 on OpenAlexaboutno aff
César García-Balaguera, Olga Yesenia García, María Victoria Gutiérrez

Bibliographic record

VenuePLOS Global Public Health · 2022
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
FundersUniversidad Cooperativa de ​Colo​mb​ia
KeywordsObservational studyQuarter (Canadian coin)Health carePsychological interventionPandemicCoronavirus disease 2019 (COVID-19)PopulationObservational methods in psychologyFamily medicineSocial mediaPsychologyNursingMedicineEnvironmental healthGeographyDiseasePolitical scienceComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

This study explores the community perceptions of COVID-19 and the healthcare system's response to it.: A web-based descriptive observational study was conducted on the general population during the third quarter of 2020 through the application of a survey via social media. Of the sample, 55% have minimal connection with prevention programs, while 66.3% received little or no information about COVID-19, and 69.62% were considered at risk of getting sick from COVID-19. Further, 73.14% were afraid to go to healthcare centers fearing the risk of becoming infected by COVID-19. The low-income population is at greater risk (OR 4.32), as well as those who have not been informed by their insurer of the risks of COVID-19 (OR 2.18). There is a need to strengthen the healthcare system and the quality and design of effective self-care educational interventions during the pandemic.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.289
GPT teacher head0.447
Teacher spread0.158 · 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
Published2022
Admission routes1
Has abstractyes

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