Relationship maintenance strategies of rotational workers and their partners
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".