Bibliographic record
Abstract
The Pathways Model identifies clinically distinct subgroups of gamblers who exhibit common, overt cardinal symptoms, but who, at the same time, differ significantly with respect to premorbid psychopathology, childhood history, and neurobiological functioning. The model proposes a conceptual frame-work that integrates research data and clinical observation to provide a structure to assist clinicians in identifying and separating distinct subgroups of gamblers that require different management strategies. While all youth gamblers are subject to ecological variables, operant and classical conditioning and cognitive processes, differences between subgroups have significant implications for diagnosis and treatment. Pathway 1 youth gamblers are essentially normal in character but simply lose control over gambling in response to effects surrounding the probability of a win. In contrast, Pathway 2 gamblers are characterized by disturbed family and personal histories, affective instability, and poor coping and problem-solving skills. They gamble as a means of emotional escape and mood regulation. Finally, Pathway 3 gamblers exhibit biological vulnerability toward impulsivity and arousal-seeking, early onset of gambling, attentional deficits, antisocial traits, and poor response to treatment. Empirical research is needed to determine the relative proportion of youth in each pathway. However, identifying the appropriate pathway for youth gamblers by the characteristics presented should provide a practical and useful clinical guide that will ultimately improve the effectiveness of treatment interventions by refining diagnostic processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".