Online help for problem gambling: Why it is and is not being considered
Bibliographic record
Abstract
Despite an increasing prevalence of gambling problems, evidence suggests that most people do not receive help for their problems. The issue of stigma has been cited as a contributing factor. Technological advances have now made it possible for individuals who are concerned about stigma to seek help for their problems without making any personal disclosures. In this way, the inherent advantages of the Internet (privacy, convenience, safety and portability) help to ensure that assistance for problem gamblers is always available and that concerns about stigma are neutralized. Unfortunately, many who might benefit from Internet-based help are unaware of these possibilities, and treatment specialists and other health-care professionals may not direct problem gamblers to these services. This paper considers: 1. What is available to problem gamblers through the Internet? 2. What is known about the efficacy of such services?, and 3. Possible reasons why problem gamblers have not been referred to the Internet by point-of-entry personnel. Implications for future action will be discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".