Bibliographic record
Abstract
Substance use, for the purpose of this study, has been defined as the non-medical use of substances (tobacco, alcohol, cannabis, cocaine, amphetamines-type stimulants, inhalants, sedatives or sleeping pills, hallucinogens, and opioids). Although there are several personality theories as to why individuals engage in substance use, there has yet to be a study conducted looking at correlations between substance use and the HEXACO model of personality. Previous research has indicated associations between substance use and personality, such as cocaine use being correlated with Extraversion and Openness to Experiences. It has also been shown that an individuals’ confidence in recovery rate correlated with higher scores in Agreeableness, Extraversion, and Conscientiousness. However, these previous studies used inconsistent and outdated personality measurements and definitions of substance use. Our study examines whether or not certain personality traits are associated with substance of choice. We used various social media outlets to administer the HEXACO Personality Inventory, a modified version of ASSIST, and the PHQ-9 on a sample of 291 individuals. Participants ranged in age from 17 to 64 and lived across North America. Along with correlating personality traits and substances, this study looked to measure personality traits in relation to seeking treatment. Results indicated statistical significance between individuals who used the same substances with Honesty/Humility and Conscientiousness, and no statistically significant correlations with treatment and personality traits. Although research in this field is not relatively new, it is important to replicate studies with up-to-date measurements in order to gain the most accurate measurement of possible correlations. * Indicates faculty mentor
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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".