Relationship functioning following a large-scale sacrifice: Perceived partner prosociality buffers attachment insecurity.
Bibliographic record
Abstract
In a sample of 229 individuals who recently undertook a large-scale sacrifice by relocating for their romantic partner's job or schooling (i.e., accompanying partners), we tested preregistered predictions linking accompanying partners' attachment insecurities (i.e., attachment anxiety and attachment avoidance) and relationship functioning (i.e., relationship quality, relationship conflict, and move-related relationship benefits). We also examined whether any negative associations found among accompanying partners' attachment insecurities and relationship functioning could be buffered by partner prosociality in the form of perceived partner gratitude (i.e., partners' expressions of move-related and general gratitude) and perceived partner sacrifice (i.e., partners' daily sacrifice behaviors and general willingness to sacrifice). Results showed that more insecurely attached accompanying partners reported worse relationship functioning after moving than their secure counterparts. Although gratitude and sacrifice did not buffer insecurely attached individuals' relationship conflict, both perceived partner general gratitude and willingness to sacrifice partially buffered avoidantly attached individuals from experiencing lower relationship quality, while move-related gratitude helped avoidantly attached individuals to feel that the move benefitted their relationship. Meanwhile, perceived partner sacrifice behaviors buffered anxiously attached individuals from experiencing lower relationship quality. This is the first study to demonstrate, in an ecologically valid sample, the implications of a large-scale sacrifice for insecurely attached accompanying partners' relationship functioning, as well as the protective effects of perceiving a partner's prosociality following the major life transition of job relocation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.003 |
| 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) | 0.001 | 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".