Gambling and the COVID-19 pandemic in the province of Quebec (Canada): protocol for a mixed-methods study
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has major collateral impacts on mental health. Gambling is among the major public health issues that seems to have been transformed by the pandemic. In the province of Quebec in Canada, gambling is an important leisure activity. About two out of three adults are in Quebec gamble. The objective of this study is to draw a portrait of the impacts of the COVID-19 pandemic on gamblers and to learn more about their experiences during the pandemic in the province of Quebec. METHOD AND ANALYSIS: This study has a sequential explanatory mixed-method design in two phases. The first phase is a cross-sectional online survey with Quebec residents who are 18 years of age or older and have gambled at least once in the previous 12 months. The second phase will be a qualitative study. Semistructured interviews will be conducted with gamblers, family members, addiction counsellors and state representatives selected through purposing sampling. ETHICS AND DISSEMINATION: This study is one of the first mixed-methods studies on the impacts of the COVID-19 pandemic on gambling. This study will generate new scientific knowledge on a worrisome public health issue, that is, gambling, and provide a better understanding of the experiences and gambling behaviours of gamblers during the pandemic. This study is funded by the Ministry of Health and Social Services of the Government of Quebec and was approved on 27 October 2020 by the Scientific and Research Ethics Committee of the CIUSSS de l'Estrie-CHUS. This is a 2-year study that will be completed in June 2022.
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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 | 0.055 | 0.033 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.083 | 0.012 |
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".