Examining the impact of mobile gambling harm minimisation features: a dualistic model of passion perspective
Bibliographic record
Abstract
Driven by the ubiquity of smartphones, sports gambling has intensified globally. Most mobile gambling apps are mandated to offer harm minimisation features which are IT tools designed to help prevent harmful gambling activity. Existing research on the effectiveness of gambling harm minimisation features often overlooks the fact that individuals engage with multiple IT tools to varying extents to achieve a single goal. As an initial step, and to reflect actual user engagement, we conduct an exploratory factor analysis on a range of opt-in harm minimisation features. Next, aligned with the dualistic model of passion, we theorise and empirical test how direct and indirect harm minimisation features moderate the translation of different passions for mobile gambling into the well-being outcome of subjective vitality. Our findings suggest that indirect harm minimisation features, but not direct features, are effective in protecting the well-being of obsessively passionate mobile gamblers. For harmoniously passionate mobile gamblers, the opposite situation holds – direct harm minimisation features strengthen the effect of a harmonious passion on vitality whereas indirect features have no significant effect.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".