Watch and yearn? Effects of watching gambling livestreams on cravings
Bibliographic record
Abstract
Background: Gambling content on streaming platforms has gained popularity. Given their intense, cue-laden nature, watching gambling streams may trigger cravings among viewers. At the same time, people who gamble may be motivated to watch gambling streams in an attempt to regulate their cravings. Methods: We tested these ideas across two preregistered online studies, recruiting i) people who gamble to compare a subgroup of gambling stream viewers with non-viewers (Study 1; nviewers = 221, nnon-viewers = 642), and ii) a group of gambling stream viewers (Study 2; nviewers = 271). Results: Gambling stream viewers were younger, tended to identify as men, and displayed higher levels of problem gambling and gambling cravings compared to non-viewers. Problem gambling severity was correlated positively with both the motivation to use gambling streams to regulate cravings and with cravings elicited by watching gambling streams. Discussion: Our findings indicate that while viewers with higher levels of problem gambling may use gambling streams to regulate their cravings, doing so might evoke cravings.
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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".