Bibliographic record
Abstract
This qualitative project explored Canadian postsecondary students' motivations for cannabis use, with a particular focus on the use of cannabis to manage psychological well-being. Participants (N = 14) were emerging adult postsecondary students in Newfoundland and Labrador (NL) who used cannabis to manage mental health, recruited through postings by NL postsecondary institutions. Data were collected using semi-structured interviews and analyzed using inductive thematic analysis. The first study explored students' motivations for using cannabis and the associated effects. Students described diverse cannabis motives, including relaxation, socializing, experimentation, productivity and therapeutic motives (i.e., managing mental or physical health). Participants also detailed secondary consequences of cannabis, including adverse interpersonal, motivational, psychological and physical health impacts. The second study explored therapeutic cannabis motives in further detail. Students used cannabis to manage cognitive and emotional states like stress, anxiety, difficult emotions and racing thoughts. Cannabis was also used to manage sleep, pain, nausea and appetite. Students also described factors contributing to increased cannabis use, including changes in routine, worsening health symptoms and academic pressure. The findings further knowledge of postsecondary students' cannabis motives, which may inform the prevention and treatment of cannabis use problems. Postsecondary students require further education on the health impacts of cannabis use and alternative coping strategies for managing stress and psychological well-being.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".