Outcome expectancy liking: A self-generated, self-coded measure predicts adolescent substance use trajectories.
Bibliographic record
Abstract
This study tested the predictive validity of a novel, brief, and easy-to-use self-report measure of expectancies and their subjective values for alcohol and marijuana use. Canadian students in Grades 7 to 11 were administered paper-and-pencil questionnaires once per year for 3 consecutive years (Krank et al., 2011). As part of the questionnaire, participants completed an outcome expectancy measure where they were asked to list 3 or 4 things they expected would happen if they used a particular substance (i.e., alcohol, marijuana) and to indicate for each whether they would or would not like this outcome. "Liking" outcomes were coded as +1, "not like" as -1, and summed to obtain an outcome expectancy liking (OEL) sum for each participant and each substance. Participants also completed substance use behavior questions for alcohol and marijuana. Multilevel modeling demonstrated that OEL sum significantly predicted the intercept and slope of substance use trajectories by participants, even when demographic variables were controlled. For both alcohol and marijuana, multilevel modeling analyses indicated that a more positive OEL sum for a substance in the first year of the study were more likely to have tried that substance earlier (intercept) and were more likely to escalate their use at a greater rate over time (slope). The results complement the predictive validity found with other direct and indirect measures of substance use associations. The outcome expectancy liking task is a simple and unobtrusive method for identifying adolescents who are at risk for early substance abuse.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".