Trends in Lower-Risk Gambling by Age and Net Income among Finnish Men and Women in 2011, 2015, and 2019
Bibliographic record
Abstract
Lower-risk Gambling Guidelines (LRGGs) were developed in Canada to reduce the risk of gambling-related harm. The LRGGs, published in 2021, consist of three limits: gamble no more than 1% of household income per month; gamble no more than four days per month; and avoid regularly gambling at more than two types of games. All three limits should be followed at the same time. This study focuses on the situation in Finland before the LRGGs were published. The aim of this study is to investigate trends in lower-risk gambling by age and net income among men and women in the Finnish adult population in 2011, 2015, and 2019. Data were drawn from cross-sectional Finnish Gambling population surveys, including permanent residents in Mainland Finland aged 15-74 with Finnish, Swedish or Sámi as their mother tongue (2011; n = 4,484, 2015; n = 4,515, and 2019; n = 3,994). The results showed an increase in the prevalence of lower-risk gambling, rising from 29% in 2011 to 39% in 2019. This upward trend was observed among both men and women, with the prevalence among men increasing from 23 to 33%, and among women from 34 to 45%. The lowest prevalence of lower-risk gambling was found among individuals aged 60-74, especially regarding expenditure guidelines, as well as among women in the lowest income tertile. In conclusion, although the prevalence of lower-risk gambling has increased in Finland, there is still potential for further improvement to minimize harm.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".