An Open Trial of a Brief, Self-Compassion Intervention Targeting Thwarted Belongingness and Perceived Burdensomeness
Bibliographic record
Abstract
OBJECTIVE: Suicide is a global health concern and developing brief and accessible interventions that can reduce suicide risk is crucial. Thwarted belongingness (TB; i.e., feeling like one doesn't belong) and perceived burdensomeness (PB; i.e., feeling like one is a burden on others) are associated with suicidality, and changes in these constructs predict changes in suicidal thoughts and behaviors. Self-compassion is a multifaceted construct that involves being open and kind to oneself and can be taught through brief writing tasks. Low self-compassion has been associated with TB, PB, and suicidal ideation, suggesting that enhancing self-compassion may decrease suicide risk. Thus, we conducted an open trial of a brief, online self-compassion intervention targeting TB and PB. METHOD: = 132) viewed an educational video on self-compassion and completed self-compassion writing tasks over the course of one week. RESULTS: Reactions to the intervention were positive, and participants reported significantly higher self-compassion scores following the intervention. However, TB and PB scores did not change from the baseline to the post-intervention assessment. CONCLUSIONS: This open trial demonstrated the feasibility and acceptability of a fully online, brief self-compassion intervention, but its impact on reducing suicide risk should be assessed further using a randomized controlled design.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".