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Record W4378904021 · doi:10.1111/pere.12504

Relationship maintenance strategies of rotational workers and their partners

2023· article· en· W4378904021 on OpenAlexafffund
Kathryn Malcom, Scott T. Ronis

Bibliographic record

VenuePersonal Relationships · 2023
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversity of New Brunswick
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyQuality (philosophy)GenerositySocial psychologyPopulationDevelopmental psychologyDemographySociologyPhysicsPolitical science

Abstract

fetched live from OpenAlex

Abstract Although individuals who reside permanently in one location and work temporarily in another (i.e., rotational workers) represent a sizeable segment of the population, they are understudied in the empirical literature. Because rotational workers and their at‐home partners have unique long‐distance relationships due to frequent separations and reunions, they and their relationships should be examined. The primary aim of this study was to identify key factors associated with maintenance of romantic relationships between rotational workers and their at‐home partners. Participants ( N = 289) were rotational workers ( n = 129) and at‐home partners of workers ( n = 160) who completed online surveys on individual, dyadic, and extra‐dyadic relationship maintenance behaviors and relationship characteristics over the course of two working‐reunion (roster) phases. Results indicated individual, dyadic, and extra‐dyadic behaviors positively predicted perceived relationship quality among partners and workers. Among partners, generosity positively predicted relationship quality at the first reunion and second departure phases. All other individual, dyadic, and extra‐dyadic relationship maintenance behaviors predicted relationship quality, regardless of the roster phase. Overall, results suggest the importance of relationship maintenance education for individuals in rotational romantic relationships.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.093
GPT teacher head0.403
Teacher spread0.310 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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