Gambling activity in the old-age general population
Bibliographic record
Abstract
Abstract Old age constitutes a vulnerable stage for developing gambling-related problems. The aims of the study were to identify patterns of gambling habits in elderly participants from the general population, and to assess socio-demographic and clinical variables related to the severity of the gambling behaviours. The sample included N = 361 participants aged in the 50–90 years range. A broad assessment included socio-demographic variables, gambling profile and psychopathological state. The percentage of participants who reported an absence of gambling activities was 35.5 per cent, while 46.0 per cent reported only non-strategic gambling, 2.2 per cent only strategic gambling and 16.3 per cent both non-strategic plus strategic gambling. Gambling form with highest prevalence was lotteries (60.4%), followed by pools (13.9%) and bingo (11.9%). The prevalence of gambling disorder was 1.4 per cent, and 8.0 per cent of participants were at a problematic gambling level. Onset of gambling activities was younger for men, and male participants also reached a higher mean for the bets per gambling-episode and the number of total gambling activities. Risk factors for gambling severity in the sample were not being born in Spain and a higher number of cumulative lifetime life events, and gambling severity was associated with a higher prevalence of tobacco and alcohol abuse and with worse psychopathological state. Results are particularly useful for the development of reliable screening tools and for the design of effective prevention programmes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".