Gambling marketing in Finland: Experiences of exposure and regulation among Finnish individuals who gamble regularly
Bibliographic record
Abstract
Aims: Gambling marketing can increase intentions to gamble, gambling consumption and gambling harms. In Finland, a substantial part of the population is at risk of gambling problems. Currently, the Finnish gambling marketing environment is changing: public health concerns have resulted in recent restrictions in gambling marketing as well as other measures to reduce gambling harm. However, the upcoming licensing system will soon significantly increase the availability and visibility of gambling by liberalising regulations. This study focuses on exposure to marketing and opinions on marketing regulations in a country with a changing regulatory landscape. Methods: This study presents results of an online survey ( N =132) amongst Finnish individuals who gamble regularly, focusing on perceptions and experiences with gambling marketing. The quantitative data are analysed using cross-tabulation and bivariate correlations, and the qualitative data by using thematic content analysis. Results: Despite recent marketing restrictions, participants were widely exposed to and identified various forms of marketing by both the monopoly and offshore operators. Marketing by offshore providers was described as widespread and aggressive. The monopoly operator actively leveraged public health messaging as a form of marketing. Of the participants, 60.5% indicated wanting stricter marketing regulations or a blanket ban on gambling marketing. Conclusions: Gambling marketing is ubiquitous in Finland. Those who gamble are widely exposed to different forms of marketing from both the monopoly and offshore providers. The upcoming licensing system is likely to further increase the visibility of marketing. Many consumers prefer stricter regulation owing to public health concerns.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".