Gambling Behaviour and Motivation in an Urban Sample of Older Adult Gamblers
Bibliographic record
Abstract
Due to low rates of gambling participation among older adults (65+ years), little is known about gender differences in their gambling behaviour and reasons for gambling. Also, little is known about differences in their motives for different forms of gambling. Following motivational theory, the present study compared the behaviour and motivation of 41 male and 63 female gamblers in Hamilton, New Zealand where a casino was recently opened. Ages ranged from 66 to 87 years. Gambling for rewards was the strongest motivation for both sexes, followed by boredom. There were no significant gender differences, nor interactions between gender and skill/chance preferences on motivation. Regular continuous gamblers had stronger preferences for horse/dog races, scratch tickets or non-casino gaming machines, and had a higher expenditure rate than regular non-continuous gamblers who more strongly preferred Lotto. They also had significantly higher scores on curiosity, stimulation, escape and apathy. Longitudinal and observational studies were suggested to examine the impact of new casinos in towns with large numbers of older adults, and to monitor potential symptoms of problem gambling. ********** Prevalence rates of gambling among older adults (65 years of age and older) in New Zealand, Australia and North America, have been the lowest of all age groups. For example, from the 1999 representative national New Zealand Gaming Survey (Abbott & Volberg, 2000) approximately 80% of older adults gambled for money within the previous 6 months, compared to 85-88% of the other 10-yearly age groups. The prevalence rates from other countries' national samples within the previous 12 months were 74% (vs. 82-85%) in Australia (Productivity Commission, 1999), 72% (vs. 73-84%) in Canada (Marshall & Wynne, 2004), and 72% (vs. 79-90%) in the United States (Gerstein et al., 1999). Over the last few decades, the incidence or growth rate of participation in gambling in other countries has been the highest among older adults, particularly among older women (Gerstein et al., 1999; McKay, 2005; Morgan Research, 1997). In the United States, the number of older adults (65+ years) gambling had more than doubled between 1975 and 1998 (Gerstein et al., 1999), and older adults form the largest age group of annual visitors to Las Vegas (McNeilly & Burke, 2002). The incidence seems to be concomitant with the increasing availability of electronic gaming machines (EGMs), casinos and commercial lotteries (Boreham et al., 2006; Delfabbro, 2000; Govoni et al., 2001; Mckay, 2005; Morgan Research, 1997), and with their declining interest in scratch tickets, sports betting and charity events (Alberta Alcohol and Drug Commission, 1998; Munro et al., 2003). In New Zealand incidence rates among older adults have fluctuated. From a report on people's participation rates between 1985 and 2000 (Amey, 2001), Lotto participation in the national samples of older adults increased from 69% in 1990 to 77% in 1995, then dropped to 72% in 2000. From 1990 to 2000 their participation rates for scratch tickets dropped from 56% to 39%, non-casino gaming machines from 13% to 10% and housie (bingo) from 5% to 3%. Their bets on horse or dog races increased from 12 to 18%, and casino participation from 2% in 1995 when casinos were first established in New Zealand to 6% in 2000. The 1999 report (Abbott & Volberg, 2000) noted that there was a moderate increase in older adults' average monthly expenditure between the 1991 and 1999 national surveys, and a decrease for the 18-24 year age group. More recently, a gambling participation rate of about 64% was found among older adults in New Zealand (Ministry of Health, 2003), with Lotto (58%) more popular than scratch tickets (19%), track betting (9%) and non-casino EGMs (6%). Gambling activities have been dichotomized into continuous and non-continuous forms. Continuous forms of gambling include scratch tickets, EGMs, track betting and casinos, whereby winnings can be immediately risked again within the same session (Abbott, 2001). …
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".