Motivation-Matched Approach to the Treatment of Problem Gambling: A Case Series Pilot Study
Bibliographic record
Abstract
The aim of the present case series was to provide a preliminary assessment of the utility of a motivation-matched treatment for problem gamblers. On the basis of their primary underlying motivations for gambling, 6 problem gamblers received either action-motivated (n = 4) or escape-motivated (n = 2) treatment. Drawing upon a cognitive-behavioural framework, this 6-session motivation-matched treatment was designed to address gamblers' maladaptive motivations for gambling (i.e., the need or desire for "escape" or "action"), as well as the effects of conditioning and maladaptive thinking patterns unique to each gambling motive subtype. Assessments were conducted at pre-treatment, post-treatment, and 3- and 6-month follow-up. Primary outcome measures included gambling behaviour (i.e., gambling frequency, time, and money spent gambling), severity of gambling problems, and gambling-related impairment or disability; secondary outcome measures included gambling-related craving, gambling abstinence self-efficacy, positively and negatively reinforcing gambling situations, and gambling outcome expectancies. Overall, participants showed pre- to post-treatment improvements on the majority of these measures, with relatively less immediate post-treatment treatment gains observed on measures that assessed positively and negatively reinforcing gambling situations and gambling-related impairment or disability. However, treatment gains at the 3- and 6-month follow-up were shown for most participants on these latter measures as well. Findings suggest promise for this novel treatment approach. The next step in this line of research is to conduct a randomized, controlled trial to compare the efficacy of this motivation-matched treatment for disordered gambling with treatment as usual.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".