Assessment of Impaired Control Over Cannabis Consumption: Psychometric Properties of the Impaired Control Scale-Cannabis (ICS-C)
Bibliographic record
Abstract
Background: Impaired control over substance use is a construct that is central to addiction and appears to play an early role in the development of addictive behaviors. The Impaired Control Scale (ICS) was developed to measure impaired control over alcohol, which has been associated with problem drinking and alcohol-related problems in young adults. However, there is relatively less research regarding impaired control over cannabis, and currently there is a lack of a comprehensive and valid scale that specifically measures this construct. This study addresses this gap in the literature by introducing the Impaired Control Scale-Cannabis (ICS-C), an adaptation of the ICS designed to measure impaired control over cannabis. We conducted a preliminary examination of the factor structure, reliability, and validity of the ICS-C. Methods: An online survey was administered to introductory psychology students (N=362; 63% women; 66% White, mean age=19.91) at two Canadian Universities who reported using cannabis at least once in the past month (average frequency = 9.34 days; SD = 9.60). All participants completed the ICS and ICS-C along with measures of cannabis use and problems, including the Impaired Control subscale of the Marijuana Consequences Questionnaire (MACQ-IC). A subset of participants completed additional measures of impulsivity and self-regulation. Results: An exploratory factor analysis (EFA, with an oblique rotation) of the 25 items of the ICS-C yielded 3 factors, one of which was comprised solely of reverse keyed items (despite reverse coding items prior to the EFA). These items were trimmed from the measure and the EFA was rerun. Two factors emerged: Attempted Control (i.e., frequency of attempts to control cannabis use) and a factor comprised of items assessing both Failed Control (i.e., unsuccessful attempts in limiting cannabis use) and Perceived Control (i.e., beliefs about the ability to control cannabis use in the future). Given that the Failed and Perceived Control items unexpectedly loaded on the same factor, suggesting high redundancy in the concepts of Failed and Perceived control, the items assessing Perceived Control were dropped from subsequent analyses. The final solution consisted of two factors, Attempted Control (alpha=0.96) and Failed control (alpha=0.88). High correlations between the MACQ-IC and ICS-C Attempted Control (r=0.42, p<.001) and Failed Control (r=0.67, p<.001) scales provided evidence for convergent validity. Weaker correlations between ICS (alcohol version) and ICS-C Attempted Control (r=0.36, p<.001) and Failed Control (r=0.34, p<.001) scales supported discriminant validity. Concurrent validity was demonstrated based on the moderate and statistically significant correlations of the ICS-C Failed Control subscale and frequency of cannabis use (r=0.47, p<.001) and grams of cannabis used (r=0.44, p<.001). Additional evidence for concurrent and discriminant validity were also found in the patterns of correlations between the ICS-C subscales and measures of impulsivity and self-regulation. Conclusions: ICS-C is a promising tool that can be used to assess impaired control over cannabis in young adults. Future research should confirm the factor structure of the ICS-C and examine its utility to screen for impaired control in the context of prevention and early intervention for cannabis-related problems.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".