Substance use patterns among adolescents with health conditions and disabilities in British Columbia: a complex sample latent class analysis
Bibliographic record
Abstract
Adolescent substance use is a significant public health concern. In Canada, youth between 15 and 24 years had the highest rate of substance use compared to other ages and between 22.3% and 37% of youth reported recent substance use. We investigated whether patterns existed in alcohol, tobacco, marijuana, and drug use among adolescents with assorted self-reported health conditions and/or disabilities (HCD) in British Columbia, Canada. We also explored the sociodemographic characteristics or ‘profiles’ of adolescents in each HCD group. We used latent class analyses (LCA) on province-wide complex-sample data of 243,645 adolescents (population estimated from weighted sample data, including 119,358 male, 123,250 female, 1037 gender missing) from the McCreary Centre Society’s 2018 British Columbia Adolescent Health Survey (BCAHS). LCA aimed to identify the optimal number of latent classes and the associated patterns within each HCD group. Results indicated that a four-class model was the best fit: No Substances, Alcohol Only, Alcohol and Marijuana, and All Substances. No Substances accounted for a major part of subpopulation in each HCD group. Three other classes accounted for a minor part of subpopulation with approximately equal percentages. Three HCD groups (Mental or Emotional, Learning Disability and 2+ Conditions) showed a relatively low percentage of No Substances and a relatively high percentage of both Alcohol and Marijuana and All Substances. Findings showed that substance use among adolescents with HCD had unique patterns and that certain HCD groups were more apt to use substances over others. We hope the findings from this study guide the creation of substance use prevention programs for youth with HCD.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".