Barriers to and Reasons for Treatment Initiation Among Gambling Help-line Callers
Bibliographic record
Abstract
Identifying barriers to seeking treatment is essential for increasing problem gambler treatment initiation in the community, given that as few as 1 in 10 problem gamblers ever seek treatment. Further, many problem gamblers who take the initial step of contacting problem gambling help-lines do not subsequently go on to attend face-to-face treatment. There is limited research examining reasons for attending treatment among this population. This study addressed these gaps in the literature by examining barriers and attractions to treatment among callers to the State of Michigan Problem Gambling Help-line. In total, 143 callers (n = 86 women) completed the Barriers to Treatment for Problem Gambling (BTPG) questionnaire and responded to open-ended questions regarding barriers to and reasons for treatment initiation, as part of a telephone interview. Greater endorsement of barriers to treatment was associated with a lower likelihood of initiating treatment, especially perceived absence of problem and treatment unavailability. Correspondingly, problem gamblers who identified more reasons to attend treatment were more likely to attend, with positive treatment perceptions being the most influential. These findings can help get people into treatment by addressing barriers and fostering reasons for attending treatment, as well as reminding clinicians of the importance of identifying and addressing individual treatment barriers among patients with problem gambling.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".