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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".