Sobering stories: Narratives of self-redemption predict behavioral change and improved health among recovering alcoholics.
Bibliographic record
Abstract
The present research examined whether the production of a narrative containing self-redemption (wherein the narrator describes a positive personality change following a negative experience) predicts positive behavioral change. In Study 1, we compared the narratives of alcoholics who had maintained their sobriety for over 4 years with those of alcoholics who had been sober 6 months or less. When describing their last drink, the former were significantly more likely to produce a narrative containing self-redemption than the latter. In Study 2, we examined the relation between the profession of self-redemption and behavioral change using a longitudinal design, by following the newly sober alcoholics from Study 1 over time. Although indistinguishable at initial assessment, newly sober alcoholics whose narratives included self-redemption were substantially more likely to maintain sobriety in the following months, compared to newly sober alcoholics who produced nonredemptive narratives; 83% of the redemptive group maintained sobriety between assessments, compared to 44% of nonredemptive participants. Redemptive participants in Study 2 also demonstrated improved health relative to the nonredemptive group. In both studies, the effects of self-redemption on sobriety and health held after controlling for relevant personality traits, alcohol dependence, recovery program involvement, initial physical and mental health, and additional narrative themes. Collectively, these results suggest that the production of a self-redemptive narrative may stimulate prolonged behavioral change and thus indicate a potentially modifiable psychological process that exhibits a major influence on recovery from addiction.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".