Instrument Development for the FocaL Adult Gambling Screen (FLAGS-EGM): A Measurement of Risk and Problem Gambling Associated with Electronic Gambling Machines
Bibliographic record
Abstract
Previous research, based on a survey of 374 electronic machine gamblers living in Ontario, Canada, led to the selection of statements and the creation of ten constructs for the development of a new instrument, the FocaL Adult Gambling Screen for Electronic Gambling Machines (FLAGS-EGM). In this study, we used the Partial Least Squares Path Analysis form of Structural Equation Modelling to produce a hierarchical set of the ten constructs with proven predictive power for problem gambling. Receiver Operating Characteristic analysis identified cut off values for all of the constructs that predicted the target values with the desired degree of accuracy. Active gamblers were placed in five categories: No Detectable Risk, Early Risk, Intermediate Risk, Advanced Risk and Problem Gamblers. As described here, the FLAGS-EGM instrument has the potential to be applied in many situations in which identification of at-risk EGM gamblers is needed.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".