Predictive utility of the NEO-FFI for later substance experiences among 16-year-old adolescents
Bibliographic record
Abstract
The onset of substance use mostly occurs during adolescence. The aim of the present study is to investigate the relevance of personality on the basis of the NEO-Five-Factor Inventory (NEO-FFI) to future experiences with tobacco, alcohol and cannabis. The test data were derived from the baseline assessment and first follow-up of the IMAGEN study, a European multicenter and multidisciplinary research project on adolescent mental health. In the present study 1004 participants were tested. The characterization of personality was conducted with the NEO-FFI at the age of 14 (T1). The data on substance use were collected with the European School Survey Project on Alcohol and Other Drugs (ESPAD) questionnaire at the age of 16 (T2). For the statistical analysis, t-tests and univariate analyses of variance were performed. The scores of Conscientiousness at T1 were significantly lower for adolescents with tobacco, alcohol and cannabis experiences at T2. We found lower scores of Agreeableness at T1 in participants with tobacco and cannabis use at T2. Extraversion at T1 was significantly higher for adolescents with smoking experiences at T2. No significant associations between Neuroticism or Openness and future substance use were observed. Low scores of Conscientiousness and Agreeableness seem to have the greatest value for a prediction of later experiences with substance use. As the present study is the first one to examine the predictive value of the NEO-FFI for future substance use in an adolescent sample, further studies are necessary to enable a better applicability in a clinical context.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".