Burden and Help-Seeking Behaviors Linked to Problem Gambling and Gaming: Observational Quantitative and Qualitative Analysis
Bibliographic record
Abstract
BACKGROUND: Models based on the uniqueness of addiction processes between behavioral addictions are highly contentious, and the inclusion of gaming disorder in the addiction nosography remains controversial. An exploratory approach could clarify a hypothesized common and subjectively identifiable process in addictive behaviors and the necessarily different expressions of the disorder due to behavior specificities, in particular the sociocultural characteristics and profiles of users. OBJECTIVE: The aim of this study was to describe the nature of contacts to a help service by exploring commonality and specificities of burden and help-seeking for problem gambling or gaming. METHODS: This was an observational quantitative-qualitative study. We included all contacts (ie, online questions and contacts by phone or chat when the helper completed a summary) to a helpline for gamers, gamblers, and relatives over a 7-year period. We constituted a text corpus with online questions and summaries of contacts by phone or chat. We collected basic sociodemographic data, including the device used to contact the service (phone or internet), contacting the service for oneself ("user") or being a relative of a user and type of relative, gambling (yes/no), gaming (yes/no), and age and sex of the gambler/gamer. We describe the corpus descriptively and report the computerized qualitative analysis of online questions, chat, and summary of phone calls. We performed a descendant hierarchical analysis on the data. RESULTS: A total of 14,564 contacts were made to the helpline, including 10,017 users and 4547 relatives. The corpus was composed of six classes: (1) gaming specificities, (2) shared psychological distress and negative emotions, (3) the procedure for being banned from gambling, (4) the provided help, (5) gambling specificities, and (6) financial problems. CONCLUSIONS: Negative emotions and shared distress linked to gambling and gaming support current scientific consensus that these behaviors can produce psychological distress in se; however, meaningful differences were observed in core symptoms of addiction between gamers and gamblers, beyond specificities related to the behavior itself: loss of control was elicited in the class corresponding to gambling specificities and not by gamers and their relatives.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".