Extending the RENO model: Clinical and ethical applications.
Bibliographic record
Abstract
The RENO Model, first published during 2004, described a science-based framework of responsible gambling principles for a range of industry operators, health service providers, community and consumer groups, and governments. These strategic principles serve as a guide for the adoption and implementation of responsible gambling and harm-minimization initiatives. This article extends the RENO Model core principles by describing how to apply these strategies to clinical practice. This discussion examines the central tenets of the model and includes a review of (a) the ethical principles that should guide the development, implementation, and practice of RENO Model responsible gambling activities; (b) a brief consideration of the various perspectives that influence the treatment of gambling-related problems; and (c) a discussion of key applied elements of responsible gambling programs. This article advances the argument that, to maximize positive outcomes and to avoid unintended harms, clinicians should apply science-based principles to rigorously evaluate the efficacy and impact of their clinical practice activities. (PsycINFO Database Record
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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 | 0.098 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.042 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".