Kava drinking in traditional settings: Towards understanding effects on cognitive function
Bibliographic record
Abstract
BACKGROUND: Kava drinking is a tradition among Pacific Island people, although growing in popularity with other ethnicities. However, drinking substantial quantities of kava has raised concerns regarding physical manifestations of slow response and lack of precision in bodily control. These impairments can have significant consequences when after consuming large volumes of kava an individual makes a choice to drive. AIMS: The objective of this study was to measure selected cognitive functions following high traditionally consumed kava volumes (greater than 2,000 mg of kavalactones) aimed at identifying potential risks for kava drink-drivers. METHODS: The reaction and divided attention of 20 control participants was assessed against 20 active kava-drinking participants during and following a 6-hr kava session in a "naturalised" setting. Assessment measures were drawn from Vienna Test System-Traffic's test battery. RESULTS/OUTCOMES: Results showed no statistical significant difference between control and active participants at any measurement point over a 6-hr testing period regardless that the movements and speech of the active participants were observed to slow as the test session and kava consumption progressed. CONCLUSION: Inconsistencies between test results and observations during testing and by road policing officers demonstrate an urgent need for more research in this field.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".