AGRI National Project: COVID Online Panel
Bibliographic record
Abstract
The AGRI National Project (ANP; https://research.ucalgary.ca/alberta-gambling-research-institute/research/national-gambling-study) provided an unparalleled opportunity for the investigation of how gambling in Canada had been impacted by COVID. While the ANP Online Panel was intended to capture Canada-wide gambling and problem gambling, while accounting for the inter-provincial variation in legal gambling provision, the ANP COVID Online Panel was designed to extend this examination to include the impact of the COVID-19 pandemic social and economic restrictions on gambling and problem gambling. To examine the impact of the pandemic on gambling in Canada, we extended the ANP Online Panel Study, conducting two additional data collection waves. This two-wave panel study was administered by Leger and re-recruited AGRI National Project Online Panel participants. As such, the ANP online panel follow-up survey data became the baseline data for this study. For the first wave of data collection, COVID Wave 1, recruitment from ANP Online Panel follow-up participants (n = 4707), was conducted expeditiously (May 14th – June 1st, 2020). Data collection began one month after the nation-wide ‘lockdown’ began and concluded while all of the provinces were still enforcing these widespread social and economic restrictions. A total of n = 3449 participants completed the COVID Wave 1 survey. Six-months later, the COVID Wave 2 recruitment began, and participants who had completed the COVID Wave 1 survey were invited to participate. The COVID Wave 2 data collection period took place between the 1st and 20th of December 2020, after the easement of nation-wide COVID restrictions. During this Wave 2 data collection period however, while the nation-wide lockdown was repealed, many provinces were still instituting some restrictions. Gambling venues for example, were open but reduced capacity to adhere to social distancing space requirements. Nonetheless, the COVID Wave 2 data collection was designed to determine what pandemic lockdown related changes were enduring. Furthermore, together with the ANP Online Panel follow-up data as a baseline, this study becomes an ABA design, with a large and stratified sample. COVID Wave 2 data, as an added benefit, provides the AGRI National Project with an appropriately timed third annual data collection. Additional information on sampling, retention, study variables, and survey questionnaires can be located in the accompanying user manual and codebooks. The manual and codebooks were created by Rokelle T. Shaw and Carrie A. Shaw.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.347 | 0.119 |
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".