The InterSECT Framework: a proposed model for explaining population-level trends in substance use and emotional concerns
Bibliographic record
Abstract
Across high-income countries, adolescent emotional concerns have been increasing in prevalence over the past two decades and it is unclear why this is occurring, including whether and how substance use relates to these changing trends. On the other hand, substance use has been generally declining, and little is known about the role of emotional concerns in these trends. Several studies have explored the changes in co-occurring substance use and emotional concerns among adolescents over time, with mixed results and inconsistent messaging about the implications of the findings. In response, we developed a theoretical framework for exploring the intersection between trends in substance use and emotional concerns (InterSECT Framework). This framework includes a discussion and related examples for 3 core hypotheses: (1) strengthening of co-occurrence, or the "hardening" hypothesis; (2) co-occurrence staying the same, or the "consistency" hypothesis; and (3) weakening of co-occurrence, or the "decoupling" hypothesis. This framework seeks to guide the conceptualization, evaluation, and understanding of changes in the co-occurrence of substance use and emotional concerns over time, including outlining a research agenda informed by pre-existing research and youth perspectives.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".