Pilot randomized controlled trial of an online intervention for problem gamblers
Bibliographic record
Abstract
INTRODUCTION: This pilot randomized controlled trial sought to evaluate whether an online intervention for problem gambling could lead to improved gambling outcomes compared to a no intervention control. Participants were recruited through a crowdsourcing platform. METHODS: Participants were recruited to complete an online survey about their gambling through the Mechanical Turk platform. Those who scored 5 or more on the Problem Gambling Severity Index and were thinking about quitting or reducing their gambling were invited to complete 6-week and 6-month follow-ups. Each potential participant who agreed was sent a unique password. Participants who used their password to log onto the study portal were randomized to either access an online intervention for gambling or to a no intervention control. RESULTS: A total of 321 participants were recruited, of which 87% and 88% were followed-up at 6 weeks and 6 months, respectively. Outcome analyses revealed that, while there were reductions in gambling from baseline to follow-ups, there was no significant observable impact of the online gambling intervention, as compared to a no intervention control condition. CONCLUSIONS: While the current trial observed no impact of the intervention, replication is merited with a larger sample size, and with participants who are not recruited through a crowdsourcing platform.Trial registration: ClinicalTrials.govNCT03124589.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 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".