Characteristics and Experiences of Employees who Gamble at Work
Bibliographic record
Abstract
Given that little is currently known about gambling in the workplace, we conducted a mixed-methods study to describe the characteristics and experiences of people who gamble at work. We administered a Canada-wide online survey (n = 2,000) of adults who 1) gamble, 2) are currently employed full-time, and 3) have internet access at work. A descriptive analysis of quantitative survey data showed that individuals who gamble at work had lower job satisfaction and higher rates of problem gambling compared to those who do not. Among those who gamble at work, we quantitatively described the types of gambling, the consequences experienced, and the motivations for gambling. Qualitative interviews were conducted with 18 individuals who met the criteria for problem gambling and who gamble at work. Data were integrated to provide a richer description of the experiences of those who gamble at work, including their motivations, the role of work–life satisfaction, and the dynamic influence of work as a social context. Motivations for workplace gambling included excitement, social connection, avoidance, and coping with stress or emotions. The results highlight the importance of understanding the varied motivations of individuals who gamble at work, and the role of work experiences in shaping meaning regarding gambling behaviours.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".