Problem Gambling in New Parents during COVID-19: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: Within the general population, research has found that problematic gambling increased during the COVID-19 pandemic. While 25-50% of people with gambling problems have dependent children, no research to date explores the role of COVID-19 and gambling among parents. Consequently, the primary aim of the current study is to assess the relationship between COVID-19 distress and gambling in new parents using a retrospective cohort study to see how these two phenomena are related and whether they are linked through parenting-related distress.\nMethods: A retrospective cohort design will be used to assess the relationship between problem gambling behaviours, mental health concerns, and parenting distress amongst postpartum women and their partners. Participants were recruited from a longitudinal cohort of postpartum women across Canada during COVID-19 (https://www.pregnancyduringthepandemic.com/). Participants will complete self-report measures to assess mental health concerns, problem gambling behaviours, and parenting distress. Assessments were completed between 1-3 years postpartum.\nResults: We expect to find that 1) parents who experienced higher COVID-19 distress will be more susceptible to later problematic gambling and that 2) this relationship between COVID-19 distress and problematic gambling may be moderated by parenting-related distress. Implications: Findings from this research will help inform early interventions to help reduce problem gambling in parents and to improve the emotional, physical, and social trajectories of their children.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".