Cannabis use, mental health, and problematic Internet use in Quebec: A study protocol
Bibliographic record
Abstract
BACKGROUND: Problematic Internet use is characterized by excessive use of online platforms that can result in social isolation, family problems, psychological distress, and even suicide. Problematic Internet use has been associated with cannabis use disorder, however knowledge on the adult population remains limited. In Quebec, cannabis use has significatively increased since 2018, and it is associated with various risks in public safety, public health, and mental health. This study aims to identify factors associated with problematic Internet use among adult cannabis users and to better understand their experiences. METHOD: This project is a mixed explanatory sequential study consisting of two phases. Phase 1 (n = 1500) will be a cross-sectional correlational study using probability sampling to examine variables that predispose individuals to problematic Internet use, characteristics associated with cannabis use, Internet use, and the mental health profile of adult cannabis users in Quebec. Descriptive analyses and regression models will be used to determine the relationship between cannabis use and Internet use. Phase 2 (n = 45) will be a descriptive qualitative study in the form of semi-structured interviews aimed at better understanding the experience and background of cannabis users with probable problematic Internet use. DISCUSSION: The results of this study will support the development of public policies and interventions for the targeted population, by formulating courses of action that contribute to the prevention and reduction of harms associated with cannabis use and problematic Internet use. Furthermore, an integrated knowledge mobilization plan will aid in the large-scale dissemination of information that can result useful to decision-makers, practitioners, members of the scientific community, and the general population regarding the use of cannabis and the Internet.
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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.005 |
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".