An exploratory study of multiple distinct gambling trajectories in emerging adults
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study uses data from a four-wave longitudinal survey of emerging adults (18–20 years of age in Wave 1) to examine whether there are multiple distinct trajectories of problem gambling risk severity and whether membership in these trajectory classes can be predicted by certain risk and protective factors. Four trajectory classes of gambling risk severity were identified – nonproblem-diminishing (73.9%), low-risk-stable (16.8%), marginal/nongambler-diminishing (7.1%), moderate-risk-increasing (2.2%) – with most youths' gambling involvement remaining stable or diminishing across the years and only the smallest most at-risk group showing a slight increase in severity across this transitional period. Three risk factors were significant predictors of class membership – being male, scoring higher on alcohol dependence, and escape-avoidance coping were all associated with increased probability of being in one of the more gambling involved trajectory classes, while lower alcohol dependence scores were associated with increased likelihood of being in the marginal/nongambling class.
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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) | 0.001 | 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 it