Examining the association between affective disorders with psychotic features and cannabis use 30 days prior to admission to inpatient psychiatry in Ontario, Canada from 2015-2019.
Bibliographic record
Abstract
Background: Cannabis use is associated with the risk of developing psychosis. There is substantial research on the association between cannabis use and non-affective psychotic disorders, but few studies have examined the relationship between cannabis and affective disorder with psychotic features (ADPF). Objectives: To investigate the association between ADPF and cannabis use 30 days prior to admission to inpatient psychiatry and to explore the role of age and gender as effect modifiers. Methods: Data from the Ontario Mental Health Reporting System collected between 2016-2019 were used to conduct multivariable regression analyses. Binary logistic regression analyses were performed to investigate whether the odds of having used cannabis were greater among those with ADPF compared to those without ADPF and whether the association was moderated by age or gender. Results: Among those with affective disorders, those with psychotic features were at no greater odds of having used cannabis 30 days prior to inpatient psychiatric admission compared to those without psychotic features. Gender was found to modify the association between ADPF and cannabis exposure. Being female with ADPF was associated with lower odds of using cannabis prior to inpatient psychiatric admission than females without ADPF. Compared to males without ADPF, males with ADPF were at no greater odds of having used cannabis within 30 days prior to admission. Overall, a larger proportion of males used cannabis prior to admission, compared to females. Age was not found to modify the association between ADPF and cannabis exposure. Conclusion: In addressing gaps in the literature regarding cannabis use and affective psychotic disorders, the results of the study demonstrated a nuanced gender-based relationship between ADPF and cannabis use. Based on these findings, the study has implications for informing early intervention initiatives for harm reduction and clinical practice among persons with severe mental health concerns.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".