A Self-Determination Theory Perspective on Motivational Interviewing for Emerging Adults Who Use Cannabis
Bibliographic record
Abstract
Emerging adults (EAs) between the ages of 20-to-24-years-old have the highest prevalence of past year cannabis use in Canada. It can be challenging to engage EAs in interventions to reduce their cannabis use and brief interventions have an important function as an initial step in the continuum of care. The current study involved a pilot of a single session of motivational interviewing delivered virtually. We also tested whether changes in basic psychological needs were associated with treatment, given recent theoretical models highlighting the link between motivational interviewing (MI) and Self Determination Theory (SDT). Participants were 40 EAs (ages 19-25), who engaged in high frequency cannabis use and were randomized to either a virtual MI intervention (n=20) or a control group (n=20). At a 90-day follow up, there was a significant increase in perceived competence to change cannabis use for the total sample, along with changes in basic psychological needs (autonomy satisfaction) (p = 0.00), there were no group differences in cannabis use consequences, autonomy satisfaction/frustration, competence satisfaction/frustration or relatedness satisfaction/frustration, autonomous and controlled treatment motivation, perceived competence to change one’s cannabis use, or participants’ willingness to initiate contact with follow-up treatment resources. The findings are discussed in the context of future directions for brief interventions and some of the limitations of the study, including delivery online during COVID-19, where sense of control and autonomy may have been more limited and changes to cannabis use more challenging.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| 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".