Online personalized feedback intervention to reduce risky cannabis use. Randomized controlled trial
Bibliographic record
Abstract
Given the widespread use of cannabis, and the concomitant risks associated with the drug, there is a need to increase the availability of interventions designed to reduce risky cannabis use. One promising intervention in the addictions employs personalized normative feedback to motivate change. A two-arm randomized controlled trial (RCT) was conducted in which participants who used cannabis in a risky fashion were randomly assigned to one of two groups – those who received an online personalized feedback report in addition to educational materials about risky cannabis use and those who just received the online educational materials. Follow-up assessment occurred at three- and six-months post-randomization. Outcome variables included: number of days cannabis was used in the past 30, risky cannabis use (ASSIST score of four or more), and participant estimates of the proportion of cannabis users among those of the same age and gender. A total of 744 participants with risky cannabis use were recruited for the trial using online advertisements. There were no significant differences between intervention and educational materials only groups at three- and six-month follow-ups for the outcome variables, number of days used cannabis in the last 30 (p = 0.927) and proportion of participants engaging in risky cannabis use (p = 0.557). At three and six month follow-ups, participants who received the feedback intervention were more likely than those in the educational materials group to estimate that a larger proportion of people their age and gender did not use cannabis in the last year (p = 0.028). While there was some evidence that the personalized feedback intervention modified normative perceptions about cannabis use, there did not appear to be support for the prediction that the intervention reduced cannabis consumption.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".