Treatment-relevant predictors of Machiavellianism among substance users
Bibliographic record
Abstract
Machiavellianism is a set of personality traits characterized by a callous nature, a belief in engaging in manipulative tactics for personal gain, a cynical and distrusting view of others, and pragmatically moral stance. Behaviors and views of individuals with elevated Machiavellian traits can be seen to have a marked similarity with several behaviors and views of individuals with substance use issues, making it difficult to differentiate between them. Using regression analysis, this exploratory study sought to identify underlying predictors of Machiavellianism. Substance using undergraduate students completed a series of questionnaires related to social connectedness, coping styles, motivation for treatment, and treatment expectations. Significant predictors of Machiavellianism included having an avoidant coping style, endorsing controlled motivation for stopping to use substances, having low treatment outcome expectancies, and feeling socially unconnected to others. This study is an important initial step in discerning differences between substance using individuals scoring higher and lower on a measure of Machiavellianism. With replication and extension, this line of research may usefully inform treatment planning for substance users. Future directions and treatment implications are discussed. • A significant and positive association was found between Machiavellianism and alcohol use • Having higher autonomous motivation to reduce substance use behavior predicted having a lower Machiavellianism score • Having higher positive expectancy about substance use treatment outcomes predicted lower Machiavellianism scores • Perceiving oneself to be more socially connected predicted having a lower Machiavellianism score • Employing less avoidance-coping skills predicted having a lower Machiavellianism score
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".