Investigating a Self-Management Harm Reduction Strategy for Symptoms of Attention-Deficit Hyperactive Disorder, Nicotine Dependence, Alcohol use, and Drug Use
Bibliographic record
Abstract
Abstract Introduction: The purpose of the present research was to examine the mediating effect that self-management has on the relationships between ADHD symptoms and nicotine dependence, alcohol use, and drug use among an undergraduate student population.Method: Data were drawn from undergraduate psychology students (N=141). We tested self-management as a mediator between (1) ADHD symptoms and nicotine use, (2) ADHD symptoms and alcohol use, and (3) ADHD symptoms and drug use.Results: After controlling for potential socioeconomic covariates, self-management was shown to be a significant mediator between ADHD symptoms and drug use, but not nicotine dependence or alcohol use.Conclusion: We observed that self-management was a significant mediator between ADHD symptoms and drug use, which suggests that self-management may play a role in the relationship between ADHD symptoms and drug use. Those individuals who have symptoms associated with ADHD and who also have high levels of self-management are less likely to abuse substances. This research has provided an empirical foundation for the development of harm reduction interventions to address drug use among individuals with ADHD.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".