Problematic internet use and cannabis consumption: A scoping review
Bibliographic record
Abstract
Objectives: As a growing body of research has linked cannabis consumption and problematic Internet use (PIU), more insight is needed to interpret this association. This scoping review aims to summarize the available literature on PIU and cannabis consumption and to underlie future avenues of research. Methods: We conducted an electronic search including all papers published from database inception until May 2023, using keywords related to PIU and cannabis use in the following databases: Academic Search Complete, APA PsychInfo, PubMed, SocINDEX, MEDLINE, CINAHL, and Psychology and Behavioral Sciences Collection. Studies eligible for this review had to meet the following criteria: (1) the primary theme had to be related to both PIU and cannabis consumption, (2) articles were published in a peer-reviewed journal, (3) articles were available in English or French, and (4) articles were not systematic reviews. Results: After screening 12,165 articles, 48 articles were retained for full-text reading and seven articles were included in this review. Conclusion: The available articles reveal a potential association between cannabis use and PIU, though operationalization heterogeneity challenges a conclusive interpretation of the results. Further research with improved measurement consistency is required to draw more robust conclusions.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".