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Record W4312691857 · doi:10.15309/22psd230217

PERCEPTION OF SELF-GROWTH DURING COVID-19 PANDEMIC: A CROSS-CULTURAL STUDY WITH OLDER ADULTS

2022· article· en· W4312691857 on OpenAlexaff
Sofia von Humboldt, Neyda Ma. Mendoza-Ruvalcaba, Elva Dolores Arias‐Merino, José Alberto Ribeiro-Gonçalves, Gail Low, Isabel Leal

Bibliographic record

VenuePsicologia Saúde & Doenças · 2022
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakPerceptionSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PsychologyMedicineVirologyInfectious disease (medical specialty)DiseaseOutbreakNeuroscience

Abstract

fetched live from OpenAlex

A pandemia de Covid-19 apareceu de forma global, afetando assim o auto-crescimento da populao idosa. O objetivo deste estudo consiste na identificao e anlise no auto-crescimento dos indivduos idosos de duas nacionalidades: mexicana e portuguesa. Neste estudo participaram 226 idosos, com 65 anos ou mais anos e residentes na comunidade. Foi executada um estudo qualitativo transcultural, atravs de um protocolo de entrevista semiestruturada. Os participantes foram interrogados sobre a sua perceo do seu auto-crescimento durante a pandemia. Posteriormente foi feita uma anlise de contedo e foram identificados os temas centrais. A anlise de contedos indicou os seguintes temas: (1) Partilha de experincias de vida; (2) Relao afetiva de qualidade; (3) Espiritualidade e religio; (4) Estar ativo; (5) Interesse por novos projetos; e (6) Participao cvica. Os participantes idosos com nacionalidade mexicana relataram que a partilha de experincias de vida como o tema mais relevante, enquanto para os participantes portugueses, possuir uma relao afetiva de qualidade era mais importante. Este estudo evidenciou a heterogeneidade de experincias vivenciadas por cada cultura, realando o auto-crescimento dos idosos no perodo pandmico.

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 categoriesInsufficient 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 score0.015
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.052
GPT teacher head0.413
Teacher spread0.362 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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