Identifying and Predicting Multiple Trajectories of Alcohol Dependence Symptoms in a Canadian Sample of 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
Working with data from the Manitoba Longitudinal Study of Young Adults (four cycles over 4 years; mean baseline age of 18.9 years), this study used latent class growth analysis to identify four alcohol dependence trajectory classes—low, decreasing (nondependence); high, decreasing (developmentally limited); low, increasing (adult onset); and persistently class were also identified: Being male, illicit drug use, stressful life events, impulsivity, and escape-avoidance coping style were all associated with increased probability of being in the more symptomatic trajectory classes, while self-esteem was associated with increased probability of being in the low symptom class. The results are discussed in relation to the issue of multifinality and implications for diagnoses, treatment, and prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it