Treatment-seeking precipitators in problem gambling: Analysis of data from a gambling helpline.
Bibliographic record
Abstract
Although research on treatment precipitators for problem gambling is scarce, telephone surveys have consistently shown that financial and emotional problems resulting from problem gambling are the factors which recovered or active gamblers most frequently report as treatment precipitators. The present study sought to build on previous evidence by analyzing the demographic and gambling-related information provided by gamblers calling the helpline operated by the New Mexico Council on Problem Gambling and receiving a referral to private counseling. Specifically we examined the differences between the callers who initiated treatment with a private counselor after receiving the referral (n = 223), and those who were likewise referred to counseling but did not attend the first appointment (n = 231). The 2 groups could only be distinguished by the fact that the therapy-initiating group cited family or financial problems as the reason for calling the helpline. Further analyses revealed that helpline staff also had an influence on counseling initiation. These findings, along with other differences between groups call for further research on the most effective ways of targeting problem gamblers who call a helpline so as to facilitate their progression to the action stage of change.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".