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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".