Responsibilities in gambling harm prevention and reduction
Bibliographic record
Abstract
Gambling harm prevention and reduction consists of a range of upstream and downstream solutions. Responsibilities for implementing and ensuring these tasks falls across a range of actors, including policymakers, regulators, health professionals and industry. Increased harms caused by online gambling necessitate new regulatory measures, and potentially new responsibilities for their implementation. The current study uses key informant interview data (N=10) conducted in four jurisdictions that have recently introduced a license-based online gambling market (Germany, the Netherlands, Sweden, Ontario). Our aim was to identify what kind of responsibilities for harm prevention and reduction emerge in competitive online markets, to whom responsibility for these tasks is assigned, and what kind of barriers to harm prevention exist across responsibilities. Our analysis shows that most universal responsibilities are assigned to policy makers and regulators. Selective measures aiming at those who gamble, are largely implemented in collaboration between regulators and industry. Indicated and treatment-focused measures are the shared responsibility of treatment professionals, regulators and industry. The main barriers to effective harm prevention related to conflicting interests, industry power, lacking harm prevention resources, lacking centralisation and offshore provision. We argue that improved harm prevention would require balancing existing asymmetries that relate to power, responsibilities and prioritisations.
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 | 0.060 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".