Gambling-related harms: Developing priorities for harm reduction policy setting
Bibliographic record
Abstract
As jurisdictions worldwide have overseen gambling expansion, most have implemented regulatory and public policy regimes to reduce harm. This study was conducted to specify the nature and extent of gambling-related harm that public policy efforts could prevent or mitigate in Ontario, Canada.\nResearch has historically operationalized harm from gambling as cases of disordered gambling; and policy work has focused on the prevalence and treatment of these cases. Recent work to fully conceptualize and measure gambling-related harm in individual gamblers, their families, and communities (Blaszczynski et al, 2015, Browne et al., 2016, 2017; Langham et al., 2016,) dovetailed with the desire of policy makers in Ontario to measure the return on investment (ROI) of harm reduction efforts.\nTo develop priorities for harm reduction policy-setting, investigators conducted extensive literature reviews, Delphi consensus process, in-depth interviews, and knowledge translation workshops with two informant groups: international research experts on gambling harm; and, Ontario policy leaders from ministries and agencies involved in gambling operation, regulation, and harm reduction.\nFindings outline expert opinion of effective evaluation metrics, data requirements, stakeholder roles, and harm reduction strategies. This research contributes methodological and evidentiary guidance for policy makers to identify priority harms and measure ROI from harm reduction programming.
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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 | 0.142 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.028 | 0.018 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".