Exploring psychological need satisfaction from gambling participation and the moderating influence of game preferences
Bibliographic record
Abstract
Psychological needs are satisfied through leisure participation, which in turn influences subjective well-being. The present study explored the psychological needs reported to be satisfied through gambling participation and examined associations between need satisfaction, game preferences and subjective well-being. A heterogeneous, self-selected sample of 1446 participants was recruited, through the Internet gambling provider Kindred Group Plc, for an online questionnaire survey. Five psychological need dimensions of gambling were identified, using exploratory and confirmatory factor analyses on calibration and validation samples, respectively: mastery, detachment, self-affirmation, risk and excitement, and affiliation. Challenge and mastery need satisfaction was higher for poker than for sports betting, horse racing, slots or casino table games; both self-affirmation and affiliation were also higher for poker than for sports betting and slots. By comparison, detachment was higher for slots than for sports gambling. While there were no significant variations in stress levels between the different forms of gambling, happiness ratings were lower for slots compared with sports betting and poker. This study provides insight into how distinctive patterns of play may satisfy different psychological needs and provides preliminary insights into how gambling patterns may prove adaptive or maladaptive as leisure choices.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".