Increased cannabis intake during the COVID-19 pandemic is associated with worsening of depression symptoms in people with PTSD
Bibliographic record
Abstract
BACKGROUND: Some evidence suggests substance use affects clinical outcomes in people with posttraumatic stress disorder (PTSD). However, more work is required to examine links between mental health and cannabis use in PTSD during exposure to external stressors such as the COVID-19 pandemic. This study assessed mental health factors in individuals with self-reported PTSD to: (a) determine whether stress, anxiety, and depression symptoms were associated with changes in cannabis consumption across the pandemic, and (b) to contrast the degree to which clinically significant perceived symptom worsening was associated with changes in cannabis intake. METHOD: Data were obtained as part of a larger web-based population survey from April 3rd to June 24th 2020 (i.e., first wave of the pandemic in Canada). Participants (N = 462) with self-reported PTSD completed questionnaires to assess mental health symptoms and answered questions pertaining to their cannabis intake. Participants were categorized according to whether they were using cannabis or not, and if using, whether their use frequency increased, decreased, or remained unchanged during the pandemic. RESULTS: (3) = 10.795, p = 0.013 (Cramer's V = 0.166). CONCLUSION: Overall, those who increased cannabis use during the pandemic were more prone to undergo meaningful perceived worsening of depression symptoms. Prospective investigations will be critical next steps to determine the directionality of the relationship between cannabis and depressive symptoms.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".