The protective effects of self-compassion on alcohol-related problems among first nation adolescents
Bibliographic record
Abstract
Given the disproportionate alcohol-related consequences experienced by North American Indigenous youth, there is a critical need to identify related risk and protective factors. Self-compassion, which has been found to mitigate the effects of trauma exposure, may serve as one such protective factor given the high degree of historical trauma and contemporary discrimination identified as contributing to the alcohol-related disparities experienced by Indigenous communities. However, no research has examined how self-compassion (i.e. the ability to be kind and accepting and to extend compassion toward oneself) plays a unique role in Indigenous peoples’ experiences with alcohol. First Nation adolescents between the ages of 11 and 18 living on a reserve in Eastern Canada (N = 106, Mage = 14.6, 50.0% female) completed a pencil-and-paper survey regarding their alcohol use, alcohol-related problems, and self-compassion. Self-compassion was significantly inversely associated with alcohol-related problems (b = -.51, p = .01, 95%CI [-.90, −.12], and significantly interacted with frequency of alcohol use in predicting alcohol-related problems (b = -.42, p = .04, 95%CI [-.82, −.03]). Simple slopes analyses revealed that the association between frequency of alcohol use and frequency of experiencing alcohol-related problems was significant and positive at low (b = 4.68, p < .001, 95%CI [2.62, 6.73]), but was not significant at high (b = -.29, p = .89, 95%CI [−4.35, 3.77]) levels of self-compassion. Binary logistic regression revealed that higher scores of self-compassion were associated with a lower odds of being in the high-risk group for AUD (OR = 0.90, 95%CI [0.83, 0.98], p = .02). Our results suggest self-compassion may be protective against experiencing alcohol problems in Indigenous youth and thus may be a target for behavioral interventions.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".