Gambling Behaviour and Motivation in an Urban Sample of Older Adult Gamblers
Notice bibliographique
Résumé
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). …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».