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Record W2767399693 · doi:10.1111/add.13958

Commentary on van der Maas <i>et al</i>. (2017): Going where the action is

2017· letter· en· W2767399693 on OpenAlexaboutno aff
Francis Markham, Martin Young

Bibliographic record

VenueAddiction · 2017
Typeletter
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsHarmPsychologyPopulationDemographyPsychiatrySocial psychologySociology

Abstract

fetched live from OpenAlex

Approximately three in 10 patrons leaving electronic gaming machine venues report serious gambling-related harm. Public health surveillance and harm minimization measures should be focused on these venues, because this is where problem gamblers are spatially concentrated. In an admirable study of casino bus tours in Ontario, van der Maas et al. 1 took the unusual step of going—as Erving Goffman might have put it—‘where the action is’ 2. The authors recruited study participants by undertaking venue exit surveys (i.e. intercept surveys) outside six ‘racinos’ and one casino in Ontario, Canada. They surveyed a stratified sample of venue-goers who were resident in Ontario and aged more than 54 years, and included the Problem Gambling Severity Index (PGSI) as a primary outcome measure. This survey revealed an extraordinarily high prevalence (28.8%) of combined problem and moderate risk gambling (PGSI 3 or more). This compares to an estimate of only 2.5% of adults in the general population of Ontario on the same measure 3. In addition, approximately 30% of the remaining gamblers intercepted by van der Maas et al. reported PGSI scores of 1 or 2. Only four in 10 patrons leaving these Ontario venues reported no symptoms of problem gambling. More troubling still, these data are likely to underestimate the true prevalence of problem gambling among electronic gaming machine (EGM) players. First, not all patrons who visit a casino or racino gamble on EGMs every visit. Problem gambling prevalence among those who did play EGMs during their visit is likely to be higher than 28.8%. Secondly, the van der Maas et al. sample included only older Ontario residents, a subpopulation known to have relatively low rates of gambling problems (e.g. 3, 4). Thirdly, problem gamblers play EGMs for longer than non-problem gamblers 5, meaning that an intercept survey will oversample non-problem gamblers relative to the population of those playing EGMs in these Ontario venues. Finally, problem gamblers are disinclined to respond honestly to surveys of this kind 6. In short, the van der Maas et al. PGSI 3+ estimate is likely to be very conservative. Van der Maas et al.'s findings are consistent with estimates of ‘problem gambling time shares’ derived using different methods. For example, in one study, Rodgers et al. asked respondents to a telephone survey how frequently they gambled on EGMs, how long they gambled for and their responses to the PGSI 7. Population-weighted bootstrap methods were used to estimate how many of the minutes spent gambling on EGMs were accounted for by problem gamblers. This study found that 36.8% [95% confidence interval (CI) = 27.3–49.5%) of minutes gambling on EGMs were contributed by those scoring 3 or more on the PGSI, and 65.8% (95% CI = 53.0–81.6%) of minutes were contributed by those scoring 1 or more on the PGSI. The alarmingly high prevalence of problem gambling within gambling venues has important implications. First, if approximately three in 10 gamblers within an EGM venue at any given time report gambling problems, then ‘responsible gambling’ codes of conduct for gambling venues should be radically reconsidered. For EGM venues obliged to intervene in cases of problem gambling 8, compliance would require interventions with at least every third EGM gambler. Secondly, the ubiquity of problem gambling in EGM venues suggests that harm minimization measures must be venue-orientated. However, there is currently little evidence evaluating the effectiveness of EGM harm reduction measures 9. Development and evaluation of in-venue harm reduction measures for EGMs needs to be a research priority. Thirdly, the van der Maas et al. study suggests that EGM venues should be a key site of public health surveillance. The monitoring of problem gambling via general population surveys is hampered by the small proportion of the general population who report gambling problems 10, 11. Monitoring EGM venues—the locations where problem gamblers can be found most easily—presents a promising opportunity for public health surveillance. Fourthly, the fact that problem gamblers are concentrated in EGM venues suggests that recruitment for treatment programs should focus on these spaces. Finally, as businesses, EGM venues are likely to resist scrutiny by researchers. Regulators need to consider the imposition of mandatory research participation as part of the licensing conditions of venues. Problem gamblers are concentrated heavily within EGM venues. Van der Maas et al. found that three in 10 patrons of EGM venues report significant problem gambling symptoms. Researchers and regulators should focus their efforts on EGM venues—the places ‘where the [problem gambling] action is’. F.M. has received funding from, or been employed on projects that received funding from the Australian Research Council, the Community Benefit Fund of the Northern Territory and the Australian Capital Territory Gambling and Racing Commission. His travel expenses to speak at an international conference have been paid by the Alberta Gambling Research Institute, an organization funded by the provincial government of Alberta. He is a member of the Public Health Association of Australia. M.Y. has received funding from the Australian Research Council, Gambling Research Australia and several Australian state government departments, most notably the Community Benefit Fund of the Northern Territory Government. Neither author has received funding from the gambling, tobacco or alcohol industries, nor are there any constraints on the publication of this paper.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.003

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.

Opus teacher head0.101
GPT teacher head0.396
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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