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Record W3164429964 · doi:10.1177/02654075211020127

Relationship satisfaction in the time of COVID-19: The role of shared reality in perceiving partner support for frontline health-care workers

2021· article· en· W3164429964 on OpenAlexaff
M Catalina Enestrom, John E. Lydon

Bibliographic record

VenueJournal of Social and Personal Relationships · 2021
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychologyMediationHealth careSocial psychologyCoronavirus disease 2019 (COVID-19)Medicine

Abstract

fetched live from OpenAlex

People often rely on partner support and shared reality during stressful and uncertain times. As such, the current research explored how these may relate to relationship satisfaction during the COVID-19 pandemic. To do so, 155 frontline health-care workers and their significant others completed measures of shared reality and relationship satisfaction, while also indicating their levels of perceived or provided support, respectively. We proposed that shared reality would foster partners providing and health-care workers perceiving support which would, in turn, promote greater relationship satisfaction. Overall, both shared reality and partner support were positively associated with relationship satisfaction for health-care workers and their significant others. Using the Actor-Partner Interdependence Model with Mediation (APIMeM), we found shared reality to be associated with greater relationship satisfaction through health-care workers perceiving greater support from their partner. Our research demonstrates that shared reality may be a way for people under stress to perceive greater partner support, providing relational benefits for the couple as a whole.

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)0.001
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 score0.035
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.088
GPT teacher head0.427
Teacher spread0.339 · 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

Citations19
Published2021
Admission routes1
Has abstractyes

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