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Record W4390071768 · doi:10.47611/jsr.v12i3.2036

Measuring Relationship Changes During COVID-19

2023· article· en· W4390071768 on OpenAlexaffabout
Maya Parkins, Matthew D. Johnson

Bibliographic record

VenueJournal of Student Research · 2023
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)PsychologyPerceptionSample (material)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Positive relationshipQuality of life (healthcare)Clinical psychologyDemographySocial psychologyMedicineSociologyDiseaseInternal medicine

Abstract

fetched live from OpenAlex

This study draws on data gathered from an undergraduate student sample to understand perceptions of how the onset of the COVID-19 pandemic impacted relationship functioning and how those perceptions are associated with current individual and relational well-being. Drawing on the stress process model and life course theory, we surveyed an online sample of 160 undergraduate students enrolled in Canadian universities who were in an intimate relationship during the onset of the pandemic (March 2020). Results showed that the three most common areas of couple functioning that were affected by the pandemic were time spent together, communication, and their sex lives. Those who reported the pandemic having a greater positive impact on their relationship reported higher life satisfaction, positive affect, positive relationship quality, and relationship confidence compared to those who reported the pandemic having less of a positive impact on their relationship. Results are consistent with other findings on intimate relationships during the early days 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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.009
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.543
GPT teacher head0.601
Teacher spread0.057 · 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.

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

Citations0
Published2023
Admission routes2
Has abstractyes

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