MétaCan
Menu
Back to cohort
Record W3176065284 · doi:10.31234/osf.io/e3fh4

Love in the Time of COVID: Perceived Partner Responsiveness Buffers People from Lower Relationship Quality Associated with COVID-Related Stressors

2020· preprint· en· W3176065284 on OpenAlexaff
Rhonda Nicole Balzarini, Amy Muise, Giulia Zoppolat, Alyssa Di Bartolomeo, David L. Rodrigues, María Alonso-Ferres, Betül Urgancı, Anik Debrot, Nipat Pichayayothin, Christoffer Dharma, Peilian Chi, Johan C. Karremans, Dominik Schoebi, Richard B. Slatcher

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsPublic Health OntarioUniversity of TorontoYork University
Fundersnot available
KeywordsStressorCoronavirus disease 2019 (COVID-19)PsychologyPandemicSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakQuality (philosophy)Clinical psychologySocial psychologyDevelopmental psychologyMedicineDiseaseInternal medicine

Abstract

fetched live from OpenAlex

External stressors can erode relationship quality, though little is known about what can mitigate these effects. We examined whether COVID-related stressors were associated with lower relationship quality, and whether perceived partner responsiveness—the extent to which people believe their partner understands, validates, and cares for them—buffers these effects. When people in relationships reported more COVID-related stressors they reported poorer relationship quality at the onset of the pandemic (N = 3,593 from 57 countries) and over the subsequent three months (N = 1,125). At the onset of the pandemic, most associations were buffered by perceived partner responsiveness, such that people who perceived their partners to be low in responsiveness reported poorer relationship quality when they experienced COVID-related stressors, but these associations were reduced among people who perceived their partners to be highly responsive. In some cases, these associations were buffered over the ensuing weeks of 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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, 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 score0.070
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.065
GPT teacher head0.405
Teacher spread0.340 · 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

Citations104
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicAttachment and Relationship DynamicsFrench-language works237,207