Cannabis, Behaviours, COVID-19 and a Template For International Comparisons
Bibliographic record
Abstract
Since its legalization in 2018, cannabis-related issues continue to be a focus for Canadian scholars. Two literature reviews are included in this issue: Bahji et al1 analyze an increase of cannabis consumption in Canadian households in both prelegalization and postlegalization times, based on 29 Statistics Canada surveys; Bahji and Gorelick,2 in a secondary analysis of lifetime and past year prevalence of cannabis withdrawal syndrome differentiate factors elicited with one or both prevalence metrics. A second topic of broad interest is the impact of coronavirus disease 2019 (COVID-19) on various behaviours. Purias et al3 report the results of an online questionnaire of shoppers during the pandemic with online shoppers demonstrating greater scores on 2 measures of problem shopping as well as associated sex difference with gaming involvement. In the second COVID-related article, Shaw et al4 analyze the impact of social lockdown on gambling activities. The third article, by Hawke et al,5 deals with the associated substance use in youth. These 3 papers are part of our COVID Chronicles series, so far 17 Editorials, Commentaries, or Research Articles have been published in the CJA over the last 2 years.6–17 At the time of writing this Editorial, a seventh COVID wave is announced, spurred mainly by the Omicron BA.5 variant. Further stress on our health system is looming once more including closures of emergency services in peripheral areas due to staff shortages from infections or burnouts after 2 years of relentless working conditions. This body of work calls for a comparison of the relative impact of the pandemic and related public health measures in our country versus others. Recently, The Canadian Medical Association Journal has published a noteworthy template for international comparisons.18 Ten comparator countries were chosen on the basis of similarities in economic and political models, per capita income levels and population size. Several data repositories were mined. The comparisons included the G7 countries plus Belgium, the Netherlands, Sweden, and Switzerland. The metrics used in the analysis included infections, related and excess deaths, percentage of population vaccinated, societal restrictions, and economic impact. Canada had among the most sustained and stringent policies based on the Oxford Stringency Index, that is, restrictions on internal movement, public events and gatherings, workplace closures, and international controls.19 In conclusion, I draw attention to this article as we debate our current and future policies including the many impacts in our field, be it opioid and/or methamphetamine use, overdoses, social isolation, limited access to treatment, use of telehealth, and recovery efforts, to name a few. We look forward for this COVID template to be a good platform to be emulated and amended for our international comparisons.
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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 | 0.052 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".