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Visão de estudantes de medicina sobre os resultados da pandemia de Covid-19 no currículo paralelo

2023· article· en· W4387017985 on OpenAlexaff
Mariana Xavier e Silva, Isabela Dombeck Floriani, Guilherme Silva Pedro, Diancarlos Pereira de Andrade, Izabel Cristina Meister Martins Coelho

Bibliographic record

VenueEspaço para a Saúde - Revista de Saúde Pública do Paraná · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth, Nursing, Elderly Care
Canadian institutionsSNC-Lavalin (Canada)
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Medical educationCurriculumPandemicLikert scalePsychologyHumanitiesMedicinePedagogyArt

Abstract

fetched live from OpenAlex

The Covid-19 pandemic has significantly impacted medical education, especially the parallel curriculum, which encompasses several extracurricular activities, such as academic leagues, internships, extension projects, undergraduate research projects, teaching assistance, and elective courses. This study aims at analyzing the result of the Covid-19 pandemic on the parallel curriculum from the perspective of medical students. This is an exploratory-descriptive, cross-sectional study, using an online questionnaire via Google Forms® with Likert-type scale questions. The sample consisted of 340 Medicine students, from the 4th to the 12th terms, from Curitiba, Paraná. The online modality facilitated the submission of scientific papers, as well as the accessibility to medical events, which had the highest adherence among the evaluated activities. Participation in scientific research stands out, especially for students in the basic/ clinical cycle. Therefore, most participants agree that there are adaptations to be maintained in the post-pandemic period.

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)0.012
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.005

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 head0.091
GPT teacher head0.463
Teacher spread0.372 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2023
Admission routes1
Has abstractyes

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