Social Responsibility Tools in Online Gambling: A Survey of Attitudes and Behavior among Internet Gamblers
Bibliographic record
Abstract
To date, little empirical research has focused on social responsibility in gambling. This study examined players' attitudes and behavior toward using the social responsibility tool PlayScan designed by the Swedish gaming company Svenska Spel. Via PlayScan, players have the option to utilize various social responsibility control tools (e.g., personal gaming budgets, self-diagnostic tests of gambling habits, self-exclusion options). A total of 2,348 participants took part in an online questionnaire study. Participants were clientele of the Svenska Spel online gambling Web site. Results showed that just over a quarter of players (26%) had used PlayScan. The vast majority of those who had activated PlayScan (almost 9 in 10 users) said that PlayScan was easy to use. Over half of PlayScan users (52%) said it was useful; 19% said it was not. Many features were seen as useful by online gamblers, including limit setting (70%), viewing their gambling profile (49%), self-exclusion facilities (42%), self-diagnostic problem gambling tests (46%), information and support for gambling issues (40%), and gambling profile predictions (36%). In terms of actual (as opposed to theoretical) use, over half of PlayScan users (56%) had set spending limits, 40% had taken a self-diagnostic problem gambling test, and 17% had used a self-exclusion feature.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".