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Record W4387960858 · doi:10.5477/cis/reis.184.3

Cambios sociopsicológicos determinantes desde la perspectiva de género durante la pandemia de COVID-19

2023· article· es· W4387960858 on OpenAlexaff
Arta Antonovica

Bibliographic record

VenueRevista Española de Investigaciones Sociológicas · 2023
Typearticle
Languagees
FieldHealth Professions
TopicHealth and Lifestyle Studies
Canadian institutionsWSP (Canada)
Fundersnot available
KeywordsHumanitiesCoronavirus disease 2019 (COVID-19)Political scienceLatin AmericansSociologyPhilosophyMedicine

Abstract

fetched live from OpenAlex

La crisis sanitaria por la COVID-19 introdujo cambios en las vidas de las personas que les afectaron no solo física, sino también psicológicamente. El objetivo de este artículo es descubrir factores sociopsicológicos determinantes, cuáles han cambiado más la salud mental de la población española y si han influido a ambos géneros por igual. Por tanto, se han utilizado los datos de la encuesta del Centro de Investigaciones Sociológicas titulado «Estudio n.o 3324. Efectos y consecuencias del coronavirus (IV)» (en concreto la pregunta 14). Las variables de la pregunta se han recodificado en variables ficticias para realizar un ANOVA y un análisis factorial exploratorio. En el estudio se han descubierto cinco factores determinantes que han cambiado la salud mental de la población española: «ser más empático/a», «disfrutar más del ocio», «descubrir nuevas actividades de ocio», «ser más religioso/a o espiritual» y «estar más interesado/a por el futuro». Todos han afectado más a las mujeres que a los hombres.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.454
Teacher spread0.371 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2023
Admission routes1
Has abstractyes

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