Training in self-compassion: reducing distress and facilitating behaviour change
Bibliographic record
Abstract
Evolutionary psychologists posit that a soothing system evolved to detect cues of warmth and kindness in the environment and signal that the organism is safe. Gilbert (2005) proposed that among individuals suffering from shame, mental illness, and addiction, this system is underactive but can be re-activated by compassionate mind training (CMT) exercises. Although dispositional levels of self-compassion have been found to predict well-being (Neff, 2003a), there have been no randomized controlled trials (RCTs) on the effects of training oneself in self-compassion. The current dissertation presents two RCTs of CMT-based interventions. In Study 1, Kelly, Zuroff, and Shapira (2009) randomly assigned 75 distressed acne sufferers to one of three conditions: compassionate self-soothing, resisting of self-attacks, and wait-list control. Over two weeks, participants instructed to engage in daily self-compassionate imagery and self-talk reported less shame, better psychosocial functioning, and improved acne symptoms compared to a control condition. Participants instructed to stand up to their-self-attacks with a strong, resilient image had these same outcomes but additionally reported reduced depression, particularly if they were high in self-criticism. Study 2 sought to investigate whether and for whom self-compassion training might facilitate behaviour change. Kelly, Zuroff, Foa, and Gilbert (in press) randomly assigned 126 smokers to one of four interventions. Individuals instructed to engage in self-compassionate imagery and self-talk at every urge to smoke reduced their cigarette consumption more quickly than those in the control condition, and as quickly as those who engaged in self-energizing and self-controlling imagery and self-talk. Furthermore, self-compassion training was particularly effective for participants who were ambivalent about change or self-critical at baseline, or if they had vivid imagery while performing the intervention exercises. Findings su
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".