Decision‐making and risk‐taking as predictors of health risk behaviors in the Millennium Cohort Study
Bibliographic record
Abstract
Background: Facets of decision-making and risk-taking are implicated in adolescent health risk behaviors; however, whether they may lead to adolescent engagement in substance use, gambling, and self-harm is unknown. Methods: = 8417). A computerized task-based measure of decision-making and risk-taking for reward (Cambridge Gambling Task) measured impulsivity, quality of decision-making, risk adjustment, and risk-taking (exposures) at age 14. Several health risk behaviors (outcomes) were self-reported at 14/17 years: cigarette use, electronic cigarette/vaping use, drinking, cannabis use, other illegal drug use (e.g., ecstasy), gambling, and self-harm. We conducted adjusted multinomial and logistic regression models. Results: Computerized task-based measures of greater impulsivity and risk-taking were most consistently associated with self-reported health risk behaviors at 14 and 17 years. Better quality of decision-making and risk adjustment were inconsistently associated with health outcomes at age 14; however, better risk adjustment was related to a reduced likelihood of all levels of cigarette and e-cigarette/vaping use (e.g., occasionally/regularly) when compared to nonusers. At age 14, risk-taking was associated with every self-reported health risk behavior (e.g., substance use, gambling) except for self-harm. In prospective models, relationships were attenuated, but risk-taking predicted new onset engagement in all forms of substance use except alcohol consumption and self-harm. Risk-taking was most strongly associated with other drug use (age 14: odds ratio (OR) = 11.26, 95% CI: 1.48, 86.01) and predictive of former vaping use (age 17: OR = 4.10, 95% CI: 1.43, 11.76). Conclusion: Risky betting on a computerized risk-taking task appears highly indicative of substance use and recent gambling at age 14 and predictive of new onset substance use and gambling 3 years later (age 17) for both sexes, but not self-harm.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".