Patterns of marijuana use and physical health indicators among Canadian youth
Bibliographic record
Abstract
We examine how trajectories of marijuana use in Canadian youth (ages 15 to 28) are related to physical health indicators in adolescence and young adulthood. Youth were initially recruited in 2003 (N = 662; 48% male; ages 12 to 18) and followed for six waves. Five trajectories of marijuana use (Abstainers-29%, Occasional users-27%, Decreasers-14%, Increasers-20% and Chronic users-11%) were identified. Chronic users reported more physical symptoms, poorer physical self-concept, less physical activity, poorer eating practices, less sleep, and higher number of sexual partners during adolescence than other classes. Decreasers also reported poorer physical self-concept and poorer eating practices than abstainers. Other trajectory classes showed few significant health problems. Chronic users also reported more acute health problems (i.e. serious injuries, early sexual debut, higher number of sexual partners, greater likelihood of having a STI) in young adulthood than all other classes contributing to costs of healthcare. Youth who engage in early, frequent and continued use of marijuana from adolescence to young adulthood are at-risk of physical health problems in adolescence and young adulthood.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".