Measuring Impaired Control over Cannabis Use: Initial Evaluation of the Impaired Control Scale-Cannabis (ICS-C)
Bibliographic record
Abstract
Background: Impaired control is a central concept in addiction. Impaired control over alcohol has been associated with heavy drinking and alcohol-related problems in young adults, but there is less research on impaired control over cannabis. Currently, there is no validated self-report instrument that comprehensively assesses impaired control over cannabis use. This study examined the factor structure, reliability, and validity of a new measure, the Impaired Control Scale-Cannabis (ICS-C), which was adapted from the widely used Impaired Control Scale (ICS) for alcohol. Method: The sample consisted of students at two Canadian universities who reported past-month cannabis use (N = 362; 63% women; 66% White, mean age = 19.91). Participants completed an online survey including the ICS-C and ICS, along with measures of cannabis use, cannabis problems, alcohol use, impulsivity, and self-regulation. Results: After trimming problematic and redundant items, the final exploratory factor analysis of the ICS-C items yielded two factors: Attempted Control (attempts to control cannabis use) and Failed Control (unsuccessful attempts to limit cannabis use). High correlations between the ICS-C subscales and the Impaired Control subscale of the Marijuana Consequences Questionnaire provided evidence for convergent validity. Support for concurrent and discriminant validity was observed in the associations of the ICS-C subscales with cannabis use, cannabis problems, impulsivity, self-regulation, alcohol use, and the alcohol ICS. Conclusions: The ICS-C is a promising tool for assessing impaired control over cannabis in young adults. Future research should further validate the ICS-C and examine its potential clinical utility for identifying individuals at risk for cannabis use disorder.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".