Online gambling and loneliness : A state of knowledge
Bibliographic record
Abstract
Context: The participation in online gambling (OG) is rapidly increasing in Quebec and is accompanied by numerous harms for the players, their entourage, and the communities. At the same time, the feeling of loneliness is a major public health issue, whose link with offline gambling is well documented but remains little explored for OG. What is certain is that these two realities are associated with significant consequences, and their combination raises public health concerns. To better understand this association and reduce the harms, a systematic review of the scientific knowledge on the subject is necessary. Objective and Method: A systematic review of existing studies, following the guidelines of the Joanna Briggs Institute, was conducted to analyze the nature and direction of the relationship between loneliness and participation in OG and to identify associated risk and protective factors. Key Result: The main result emerging from this synthesis of the scientific literature is that loneliness contributes to engagement in OG as well as to the development of a gambling disorder (TJHA). Psychosocial risk factors are associated with the emergence of loneliness and the incidence of TJHA in vulnerable populations. Issue: The available studies are limited and present gaps, particularly regarding the assessment of loneliness. The results highlight the need for further research to better understand the underlying mechanisms and protective factors. Action Plan and Impact: This knowledge synthesis highlights the importance of recognizing loneliness as a factor influencing engagement in OG, and even the development of TJHA, and integrating this understanding into clinical practice, health education, and public health prevention and awareness strategies, in addition to continuing research to enrich knowledge on the "loneliness-OG" relationship. Ultimately, the implementation of this action plan has the potential to improve the detection and support of people experiencing loneliness and those engaging in OG, to raise awareness among health professionals about the relationship between loneliness and OG, and to develop and strengthen the effectiveness of public health prevention campaigns to ultimately reduce the harms associated with the "loneliness-OG" synergy.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".