The impact of depressed mood and coping motives on cannabis use quantity across the menstrual cycle in those with and without pre‐menstrual dysphoric disorder
Bibliographic record
Abstract
BACKGROUND AND AIMS: Reported rates of cannabis use among Canadian females are increasing. Female cannabis users progress to cannabis use disorder more rapidly than males (telescoping) and have higher rates of emotional disorder comorbidity. Addictive behaviors may change, along with mood and motivations, across the menstrual cycle (MC), particularly for females with pre-menstrual dysphoric disorder (PMDD). This study aimed to determine whether increases in depressed mood and coping motives would predict increased cannabis use pre-menstrually/menstrually, particularly among females with PMDD. We also assessed positive mood and enhancement motive ratings to establish specificity of predicted depressed mood and coping motive results. DESIGN: Observational study using data collected across 32 days using electronic daily diary methods. SETTING: Nova Scotia, Canada. PARTICIPANTS: Sixty-nine naturally cycling female cannabis users (Mean (M) age = 29.25, Standard Deviation (SD) = 5.66) with and without retrospectively identified PMDD (via structured clinical interview) and prospectively identified PMDD (via elevated pre-menstrual depressed mood). Self-reported MC phase was validated using salivary progesterone concentrations. MEASUREMENTS: Depressed/positive mood, coping-/enhancement-motivated cannabis use, and cannabis use quantity. FINDINGS: Coping motives explained heightened cannabis use pre-menstrually/menstrually in those with retrospectively identified PMDD. Depressed mood explained increased cannabis use menstrually in those with retrospectively/prospectively identified PMDD. Moreover, prospectively identified PMDD significantly moderated the relationship between depressed mood and cannabis use quantity menstrually. In those with prospectively identified PMDD, positive mood and enhancement motives were associated with decreased cannabis use during the follicular/ovulatory phases. Females with versus without retrospectively identified PMDD also displayed greater overall cannabis use quantity (M [SD] = 3.44[2.84] standard joint equivalents versus 1.85[1.82], respectively; U = 277.50, P = 0.008). CONCLUSIONS: Depressed mood may explain heightened cannabis use menstrually in females with pre-menstrual dysphoric disorder. Coping motives may explain heightened cannabis use pre-menstrually/menstrually in females with retrospectively identified with pre-menstrual dysphoric disorder.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".