The link between sacrifice and relational and personal well-being: A meta-analysis.
Bibliographic record
Abstract
Prosocial behavior is often thought to bring benefits to individuals and relationships. Do such benefits exist when prosocial behavior is costly for the individual, such as when people are sacrificing for their partner or relationship? Although different theoretical accounts would predict that sacrifice is either positively or negatively associated with personal and relational well-being, empirical work in this regard has been inconclusive. We conducted a meta-analytic synthesis of 82 data sets and 9,547 effect sizes (N = 32,053) to test the link between sacrifice and both personal and relationship well-being for both the individual who performs the sacrifice and their romantic partner. We examined four different facets of sacrifice (i.e., willingness to sacrifice, behavioral sacrifice, satisfaction with sacrifice, and costs of sacrifice). Results revealed that these facets were differently associated with well-being. Specifically, an individual's willingness to sacrifice was positively associated with their own personal and relationship well-being and with their partner's relationship well-being (.09 < rs < .27). However, behavioral sacrifice was negatively associated with own personal well-being (r = -.07). Satisfaction with sacrifice was positively associated with individual and partner well-being (.11 < rs < .43). Costs of sacrifice were negatively related to one's own personal and relationship well-being and to the partner's relationship well-being (-.10 < rs < -.26). Some moderators were also identified. We discuss the implications of these findings for research on prosocial behavior and relationships, address the implications of the methodologies used to study prosocial behavior, and suggest directions for future research. (PsycInfo Database Record (c) 2020 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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.030 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".