Good Times, Bad Times: A Closer Look at the Relationship between Savoring and Self-Compassion
Bibliographic record
Abstract
Self-compassion is associated with many positive outcomes and is generally viewed as an adaptive way of responding during difficult times. But self-compassion has also been positively associated with savoring, a way of responding to positive experiences that involves attempting to sustain or intensify positive emotions, and little is known about the direction of this relationship or whether it generalizes to responses immediately after a positive event. We conducted two studies to learn more about the directionality (Study 1) and generalizability (Study 2) of this relationship. In Study 1, athletes (N = 298) completed assessments of self-compassion and savoring capacity at two time-points throughout a season. We tested a cross-lagged panel model and found that self-compassion predicted increases in savoring, but savoring did not predict change in self-compassion. In Study 2, sports fans (N = 244) reported the extent to which they engaged in savoring immediately after their favorite team had won a championship game. The results showed that self-compassion was positively associated with savoring following the game. Overall, this research reveals more about the directionality and generalizability of the relationship between self-compassion and savoring, and shows that the effects of self-compassion extend to the positive side of the human experience.
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 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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".