Experience of LGBTQIA2S+ populations with gambling during the COVID-19 pandemic: protocol for a mixed-methods study
Bibliographic record
Abstract
INTRODUCTION: Research undertaken since the beginning of the COVID-19 pandemic has provided us information about the impact of the pandemic on the gambling habits of the general population. However, very little is known about certain subgroups at increased risk of developing gambling disorder, such as the LGBTQIA2S+ population. The purpose of this study is to describe the impact of the COVID-19 pandemic on gambling behaviours among LGBTQIA2S+ individuals. In addition, we want to understand the experiences of the LGBTQIA2S+ population with gambling disorder and identify interventions that LGBTQIA2S+ people have found to be effective in addressing problem gambling during the COVID-19 pandemic. METHODS AND ANALYSIS: This study has a sequential explanatory mixed-method design in two phases over 2 years. The first phase is a correlational study. We will conduct a cross-sectional survey using a stratified random sampling among Canadian residents who are 18 years of age or older, self-identify as sexually and gender-diverse (ie, LGBTQIA2S+) and have gambled at least once in the previous 12 months. This survey will be administered online via a web panel (n=1500). The second phase is a qualitative study. Semistructured interviews will be conducted with LGBTQIA2S+ people with problematic gambling (n=30). ETHICS AND DISSEMINATION: This research project has been ethically and scientifically approved by the Research Ethics Committee and by the CIUSSS de l'Estrie-CHUS scientific evaluation committee on 3 March 2022 (reference number: 2022-4633-LGBTQ-JHA). Electronic and/or written informed consent, depending on the data collection format (online survey and online or in-person interviews), will be obtained from each participant. A copy of the consent form and contact information will be delivered to each participant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.070 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.068 | 0.015 |
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".