How high: Quantity as a predictor of cannabis-related problems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Research on cannabis use has emphasized frequency as a predictor of problems. Studies of other drugs reveal that frequency relates to psychological and physiological outcomes, but quantity also plays an important role. In the study of cannabis, quantity has been difficult to assess due to the wide range of products and means of consumption. METHODS: The present study introduces three new measures of quantity, and examines their contribution to cannabis-related problems. Over 5,900 adults using cannabis once or more per month completed an internet survey that inquired about use, dependence, social problems and respiratory health. In addition to detailing their frequency of cannabis use, participants also reported three measures of quantity: number of quarter ounces consumed per month, usual intensity of intoxication, and maximum intensity of intoxication. RESULTS: Frequency of use, monthly consumption, and levels of intoxication predicted respiratory symptoms, social problems and dependence. What is more, each measure of quantity accounted for significant variance in outcomes after controlling for the effects of frequency. CONCLUSION: These findings indicate that quantity is an important predictor of cannabis-related outcomes, and that the three quantity measures convey useful information about use.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it