Retrospective and prospective assessments of gambling-related behaviors across the female menstrual cycle
Bibliographic record
Abstract
BACKGROUND AND AIMS: Despite increases in female gambling, little research investigates female-specific factors affecting gambling behavior (GB). Although research suggests that some addictive behaviors may fluctuate across menstrual cycle phase (MCP), gambling requires further investigation. In two studies, we examined associations between MCP and three risky GBs: time spent gambling, money spent gambling, and the probability of consuming alcohol while gambling. Associations between MCP and negative affect were also examined in Study 2. We predicted that, consistent with self-medication theory, increases in negative affect (Study 2) and risky GBs (Studies 1 and 2) would occur premenstrually/menstrually relative to other phases. METHODS: Data were obtained from 33 female gamblers using a retrospective timeline followback procedure (Study 1) and from 20 female gamblers using a prospective 32-day, daily diary method (Study 2). In Study 2, salivary progesterone levels verified self-reported MCP validity. RESULTS: Findings revealed significant, but somewhat inconsistent, MCP effects on GBs across studies. The self-medication hypothesis was partially supported. Increases relative to another MCP(s) were found for alcohol consumption while gambling premenstrually, time spent gambling menstrually/premenstrually, money spent gambling menstrually, and negative affect premenstrually. Unexpectedly, findings more consistently indicated that GBs increased during ovulation, suggestive of enhanced reward sensitivity. Progesterone assays validated self-reported MCP (Study 2). DISCUSSION AND CONCLUSIONS: The results suggest a role of ovarian hormones on negative affect and GBs in females. This research could lead to the identification of female-specific factors affecting gambling and the development of more effective interventions for females with, or at risk for, problematic gambling.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".