Impacts of the COVID-19 pandemic on gamblers in Canada: Qualitative analysis of responses to an open-ended question
Bibliographic record
Abstract
CONTEXT: Many stakeholders have expressed concerns about the impacts of the COVID-19 pandemic on gambling practices. These have historically increased during crises, potentially leading to deleterious effects on problematic gamblers, their families, and their communities. Primary care professionals need to better understand gamblers' experience during the pandemic to offer high level of care for this population. OBJECTIVE: draw up a portrait of the experience of gamblers regarding the impacts of the COVID-19 pandemic on their gambling practices. STUDY DESIGN: Qualitative analysis. SETTING: Content analysis of responses to a single open-ended question placed at the end of a cross-sectional survey which was online from February 16 to March 15 2021 in Quebec (Canada). Participants were recruited by a non-randomised online sampling. POPULATION STUDIED: 1529 individuals participated in the study, of whom 724 answered the open-ended question. Inclusion criteria were: (1) 18 years and older (2) living in the province of Quebec, Canada (3) has gambled at least once in the past year. RESULTS: Respondents' median age is 43 years, 54% are women and 57% are problem gamblers according to the Problem Gambling Severity Index. Three main themes were identified: (1) the changes in gambling practices during the pandemic as perceived by the respondents, (2) the impacts of these changes on their lives, and (3) the factors that influenced these changes. A significant proportion of gamblers felt that their gambling practices had increased during the pandemic, mainly due to boredom and increased free time. Many of them did not report deleterious effects of this increase whereas others reported being devastated. On the opposite, the pandemic was perceived by some participants as a unique window of opportunity to decrease their problematic gambling practices. CONCLUSIONS: The pandemic has created space to fill into many individuals' lives as usual leisure activities, hobbies and spending habits became out of reach. It led to increased gambling for many participants. While many did not report deleterious effects of this increase, others expressed being at great risk and therefore need primary care professionals to be equipped to support them.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".