Not always as advertised: Different effects from viewing safer gambling (harm prevention) adverts on gambling urges
Bibliographic record
Abstract
Public concern around gambling advertising in the UK has been met not by government action but by industry self-regulations, such as a forthcoming voluntary ban on front-of-shirt gambling sponsorship in Premier League soccer. “Safer gambling” (harm prevention) adverts are one recent example, and are TV commercials which inform viewers about gambling-related harm. The present work is the first independent evaluation of safer gambling adverts by both gambling operators and a charity called GambleAware. In an online experiment, we observed the change in participants’ (N = 2,741) Gambling Urge Scale (GUS) scores after viewing either: a conventional financial inducement gambling advert, a gambling operator’s safer gambling advert, an advert from the GambleAware “bet regret” campaign, an advert from the GambleAware “stigma reduction” campaign, or a control advert that was not about gambling. Relative to a neutral control advert, GUS scores increased after viewing a financial inducement or an operator’s safer gambling advert. In comparison to the neutral control condition, GUS score changes were similar after viewing a bet regret advert, but showed a significant decrease after viewing a stigma reduction advert. Those at higher risk of harm reported larger decreases in GUS after watching a bet regret or stigma reduction advert. Overall, this study introduced a novel experimental paradigm for evaluating safer gambling adverts, uncovered a potential downside from gambling operators’ safer gambling adverts, and revealed variation in the potential effectiveness of charity-delivered safer gambling adverts.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".