The Naturalistic Cannabis Administration Protocol (NCAP): A Proof-of-Concept Study
Bibliographic record
Abstract
Lab-based studies examining the effects of cannabis administration on human behavior compromise ecological validity due to the influence of set and setting. Contextual factors of clinical settings have long been recognized as producing measurable changes in physiology, emotionality, and cognition. Among people who use drugs, these settings may be associated with higher levels of perceived stigma and stereotype threat which may meaningfully confound the effects of cannabis on outcomes of interest. Recent liberalization of cannabis regulation may allow novel and more ecologically valid approaches to assessing the acute effects of cannabis. The Naturalistic Cannabis Administration Protocol (NCAP) is a novel paradigm for the study of acute cannabis effects in an ecologically valid manner. Two independent studies demonstrated the safety and feasibility of the NCAP. Participants (N= 79; Mage = 25.44, SD = 5.67) self-administered the cannabis of their choice in their home and then (Study 1; n= 47) engaged in a leisure activity or (Study 2; n= 32) underwent cognitive assessment remotely via videoconference following cannabis administration. The NCAP was well tolerated across samples with no reported adverse events. These findings provide a rationale for the adoption of the NCAP to reduce research barriers and develop our research capabilities to fit the landscape of cannabis use today.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".