A scoping review of routinely collected linked data in research on gambling harm
Bibliographic record
Abstract
Gambling harm is a global public health challenge. Gambling is often recorded in settings using routinely collected data (RCD). Linking of existing RCD affords numerous opportunities for policy-led research on gambling harm and early intervention. To date, no previous review has examined research describing data linkage of RCD and gambling. Here, we searched for peer-reviewed articles using data linkage methodology with RCD and measures of gambling, gambling harm, or health-related outcomes. After screening 2373 articles, we conducted a narrative synthesis of the 17 included articles. Studies described data from 2,136,966 individuals, most originated from Nordic countries, adopted a range of experimental designs, tended to link individual-level data with risk factors for physical and mental health harms, and defined gambling in diverse ways. Study quality was mixed. There exist numerous opportunities for further data linkage studies with RCD to both inform public policy and understand population-wide changes in gambling.
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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 | 0.027 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.023 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".