Does self-compassion moderate the relationship between goal importance and anticipated emotion when failing to meet a goal?
Bibliographic record
Abstract
Emotions have been shown to play an important role in goal pursuit (Carver & Scheier, 1990). Researchers (Neff et al., 2005) have demonstrated that self-compassion moderates emotional reactions after goal failure. However, little research has examined the role of self-compassion on anticipated emotions when failing to reach a goal. Adults (N = 130; Mage = 38.34, SD = 11.42 years) training for a marathon or half-marathon reported goals for the upcoming race, goal importance, anticipated positive and negative emotions when failing to reach the goal, and self-compassion. Separate moderation analyses were conducted using the SPSS macro PROCESS with anticipated emotions estimated from goal importance, self-compassion, and their product. Self-compassion was a significant moderator of the relationship between goal importance and the anticipation of negative emotions (R2adj. = .27; point estimate = -.1793; BC CI = -0.2782 to -0.0804). Simple slope tests revealed a positive association between goal importance and anticipated negative emotions, but goal importance was less strongly related to anticipated negative emotions for high levels of self-compassion (b = .25, p = .006) than for moderate (b = .43, p < .001) or lower levels (b = .61, p < .001). Goal importance (point estimate = -.2299; BC CI = -.3950 to -.0649) and self-compassion (point estimate = .2499; BC CI = .1067 to .3931) were significantly associated with anticipation of positive emotions when failing to reach the goal (R2 = .12, p = .001), with no significant interaction. Based on these findings, self-compassion may promote persistence towards important goals as individuals anticipate experiencing fewer negative emotions if they fail to reach their goal.Acknowledgments: This research was supported by the Social Sciences and Humanities Research Council of Canada.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".