Self-Compassion as a Compensatory Resilience Factor for the Negative Emotional Outcomes of Alcohol- Involved Sexual Assault among Undergraduates
Bibliographic record
Abstract
Objectives: Approximately half of sexual assaults involve alcohol; these assaults tend to be more severe and may be more likely to result in negative emotional outcomes like anxiety and depression (Ullman & Najdowski, 2010). Self-compassion (SC; extending kindness and care towards oneself) may promote resilience from the negative emotional consequences of alcohol-involved sexual assault (AISA). This study examined SC as a resilience factor, testing whether it attenuates and/or counteracts the association between AISA and negative emotional outcomes. Methods: Undergraduate drinkers (N = 785) completed measures tapping past-term AISA (Kehayes, et al., 2019), SC (i.e., Self-Compassion Scale; Neff, 2003), and anxiety and depression (Kessler et al., 2002). The Self-Compassion Scale was scored as two higherorder domains (self-caring, self-criticism) each with three lower-order facets (self-kindness, mindfulness, and common humanity; over-identification, self-judgment, and isolation). Results: Supporting compensatory effects, the higher-order SC domains showed main effects: the presence of self-caring and relative absence of self-criticism counteracted the adverse effects of AISA on both anxiety and depression. Similarly, the lower-order SC facets showed main effects: the presence of self-kindness and relative absence of overidentification counteracted the adverse effects of AISA on anxiety/depression – with therelative absence of self-judgment and isolation additionally counteracting the effect of AISA on depression. Conclusion: SC works as a compensatory resilience factor for the association between AISA and anxiety/depression. Implications: SC interventions with attention towards increasing self-kindness and decreasing negative facets of SC may be important for negative emotional outcomes in general, including those following AISA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".