The Long Shadow of Addiction-Related Nostalgia: Nostalgia Predicts Ambivalence and Undermines the Benefits of Optimism in Recovery
Bibliographic record
Abstract
Background: Previous research has shown that nostalgia for the pre-addicted self can motivate people living with addiction to engage in behavior change. Objective: Herein, we explored nostalgia for the addictive behavior—labeled addiction-related nostalgia (ARN)—among people in recovery from engaging in addictive behavior. We tested the novel idea that ARN is positively associated with ambivalence about recovery. We also hypothesized that ARN may counteract the positive influence of optimism on individuals’ commitment to recovery. Results: In two studies involving individuals in recovery from a gambling (Study 1; N=301) or alcohol use disorder (Study 2; N=604), ARN was linked to increased ambivalence about recovery, while optimism was associated with decreased ambivalence. As expected, the interaction between optimism and ARN revealed that nostalgia either eliminated (Study 1) or reduced (Study 2) the negative relation between optimism and ambivalence. Conclusions: These findings underscore the challenges posed by ARN in the recovery process and emphasize the importance of interventions that address and mitigate its impact while considering the moderating role of optimism.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".