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Record W4225420806 · doi:10.1177/02654075221093611

Relationship difficulties and “technoference” during the COVID-19 pandemic

2022· article· en· W4225420806 on OpenAlexaff
Giulia Zoppolat, Francesca Righetti, Rhonda Nicole Balzarini, María Alonso-Ferres, Betül Urgancı, David L. Rodrigues, Anik Debrot, Juthatip Wiwattanapantuwong, Christoffer Dharma, Peilian Chi, Johan C. Karremans, Dominik Schoebi, Richard B. Slatcher

Bibliographic record

VenueJournal of Social and Personal Relationships · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsUniversity of Toronto
FundersStichting voor de Technische WetenschappenNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakPsychologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Social psychologyMedicineVirologyOutbreak

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has touched many aspects of people’s lives around the world, including their romantic relationships. While media outlets have reported that the pandemic is difficult for couples, empirical evidence is needed to test these claims and understand why this may be. In two highly powered studies ( N = 3271) using repeated measure and longitudinal approaches, we found that people who experienced COVID-19 related challenges (i.e., lockdown, reduced face-to-face interactions, boredom, or worry) also reported greater self and partner phone use (Study 1) and time spent on social media (Study 2), and subsequently experienced more conflict and less satisfaction in their romantic relationship. The findings provide insight into the struggles people faced in their relationships during the pandemic and suggest that the increase in screen time – a rising phenomenon due to the migration of many parts of life online – may be a challenge for couples.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0080.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.122
GPT teacher head0.348
Teacher spread0.226 · 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

Citations17
Published2022
Admission routes1
Has abstractyes

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