Efficiently Quantifying Egocentric Social Network Cannabis Use: Initial Psychometric Validation of the Brief Cannabis Social Density Assessment
Bibliographic record
Abstract
OBJECTIVE: Social environment is a key determinant of substance use, but cannabis-related social network analysis is not common, in part because of the assessment burden of comprehensive egocentric social network analysis. METHOD: = 310) using a survey-based design. The B-CaSDA assesses the quantity and frequency of cannabis use for the respondent's four closest (nonparent) relationships. RESULTS: Ego cannabis use severity was elevated for each additional person who used cannabis at all or daily in the individual's social network. B-CaSDA indices (i.e., frequency, quantity, total score) were positively correlated with cannabis consumption, cannabis use severity indicators, and established risk factors for harmful cannabis use. B-CaSDA indices also discriminated between those above and below a clinical cutoff on the Cannabis Use Disorder Identification Test-Revised (CUDIT-R). Finally, in omnibus models that included common risk factors for cannabis use severity, the B-CaSDA quantity index contributed additional variance when predicting CUDIT-R total score, and B-CaSDA frequency contributed additional variance in predicting the CUDIT-R quantity-frequency subscale. CONCLUSIONS: The results suggest that the B-CaSDA has the potential to expand social network research on cannabis use and misuse by increasing its assessment feasibility in diverse designs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".