Interpreting prevalence estimates of pathological gambling: Implications for policy
Bibliographic record
Abstract
Some guidelines for interpreting prevalence estimates for the purpose of establishing the number of pathological gamblers in the community are presented. The analysis is based on the concept of the likelihood ratio, a recommended procedure for validating criteria for defining cases based on test scores. It is shown that the likelihood ratio can be employed with available estimates of prevalence to translate cut-off scores into positive predictive value. Those cut-off scores associated with high positive predictive values provide an empirical measure of confidence that those gamblers who meet or exceed the cut-off criterion are pathological gamblers. A potential limitation of the analysis is the possible specificity of results to the validation studies employed to compute likelihood ratios and to the specific estimates of prevalence used to determine positive predictive value. A recommendation is presented for obtaining study- or community-specific validation evidence.
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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 | 0.187 | 0.649 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".