Multilevel Latent Class Profile Analysis: An Application to Stage-Sequential Patterns of Alcohol Use in a Sample of Canadian Youth
Bibliographic record
Abstract
Recently, latent class analysis (LCA) and its variants have been proposed to identify subgroups of individuals who follow similar sequential patterns of latent class membership for longitudinal study. A primary assumption underlying the family of LCA is that individual observations are independent. In many applications, however, particularly in research on adolescent substance use, individuals are often dependent because of multilevel data structure, where the unit of observation (e.g., students) is nested in higher level units (e.g., schools). In this study, we propose multilevel latent class profile analysis (MLCPA), which will allow us to analyze the longitudinal data with a multilevel structure under the framework of LCA. We apply an MLCPA using data from the COMPASS study, a 9-year study funded by the Canadian Institutes of Health Research and Health Canada, in order to identify representative sequential drinking patterns of Canadian youth and investigate whether these sequential patterns vary across schools. The MLCPA identified three common student-level drinking behaviors: non-drinker, ever lifetime, and binge drinker. The sequence of drinking behaviors can be classified into one of three longitudinal sequential patterns: non-drinking stayer, light drinking advancer, and heavy drinking advancer. In addition, MLCPA uncovered two latent clusters (low-use school and high-use school) out of 64 schools in Ontario and Alberta based on the prevalences of sequential drinking patterns.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".