Lived Experiences of Gaming and Gambling Related Harm and Implications for Healthcare Services
Bibliographic record
Abstract
Children and young people (CYP) are high consumers of loot boxes, raising concerns about the impact of a convergence of gaming and gambling-related harms and their potential negative developmental outcomes in adulthood. Especially, given evidence that practitioners and parents/carers are lacking awareness of the risks of converging gaming-gambling environments. Addressing these risks necessitates understanding the experiences of gaming and gambling-related harm within healthcare systems. This study aimed to gain insights from individuals with previous lived experience of gaming and/or gambling-related harm in the context of CYP and healthcare systems. A qualitative design was adopted using two semi-structured online focus groups, involving five participants with previous lived experience of gaming and/or gambling-related harm. Focus groups explored their experiences of healthcare services and barriers to support in the journey through harm and recovery. Thematic analysis of the data revealed five key themes: i) Escapism; ii) Identity; iii) Preventative Education; iv) Safer Environments; v) Health-based Narratives. Results suggested a convergence of gaming and gambling-related harm in terms of patterns of experiences of escapism and internalising harm with identity, highlighting the need for safer environments and preventative approaches to protect CYP against novel risks of harm through healthcare systems. The results suggest that preventative approaches need to understand the virtual worlds of CYP and the importance of digital resilience. Implications for practitioners, services, policy makers, and regulators seeking to protect CYP from the risks of gaming and gambling-related harm are discussed.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".