Research priorities in gambling: Findings of a large-scale expert study
Bibliographic record
Abstract
Objective: While gambling is a growing public health concern, research resources are limited, and no guidance is available to prioritise research. This study aimed to identify priorities for gambling research on a global scale using a systematic, transparent, and democratic methodology to inform researchers and other stakeholders. Methods: Leading gambling researchers were invited to list gambling-related research questions that can contribute to strengthening evidence-based policy, prevention, and effective early intervention and treatment of problem gambling. Suggestions were consolidated into research options and evaluated against six criteria (Answerability, Feasibility, Effectiveness, Impact on equity and an additional two based on the category of research options: Novelty and Relevance for description-type, Potential for burden reduction and Deliverability for intervention-related options). Stakeholders (n = 14) assigned relative weights to each criterion, and options were ranked according to their weighted research priority scores. Results: With input from 46.9% of eligible researchers (n = 307) from 35 countries, 1,361 questions were consolidated into 102 options. Evaluations showed strong agreement between experts, and the top 25 priorities were identified. The results highlight the need for further knowledge about the epidemiology, etiology, and consequences of problem gambling. Top-priority topics indicate the importance of focusing on vulnerable and minority groups, youth, significant others, technological innovations, advertisements, the convergence of gaming and gambling, and co-occurring conditions. Evaluating and tailoring existing measures were prioritised more highly than new interventions, and identifying factors underlying treatment seeking, drop-out and relapse was also considered a priority. Conclusions: This initiative successfully involved the global research community in identifying gambling research priorities. The results provide information for researchers and other stakeholders for future projects and funding.
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 | 0.162 | 0.232 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| 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".