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CONTROLLING GAMBLING: A POPULATION‐BASED PERSPECTIVE TO MEASUREMENT AND MONITORING AS RESOURCE FOR EFFECTIVE INTERVENTIONS

2009· letter· en· W2097717137 on OpenAlexaff
Norman Giesbrecht

Bibliographic record

VenueAddiction · 2009
Typeletter
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsPerspective (graphical)Psychological interventionPopulationResource (disambiguation)PsychologyMedicineEnvironmental healthPsychiatryComputer science

Abstract

fetched live from OpenAlex

The paper by Rodgers et al.[1] explores relatively new territory in the area of measuring gambling participation, drawing on the area of measuring alcohol consumption and related problems. It is becoming more common to draw parallels across two or more health and addiction areas. In recent years there have been several essays and analyses which have looked at parallel lessons: from the tobacco control experience for controlling obesity [2,3], tobacco control for physical inactivity [4], from tobacco control for alcohol [5], from alcohol control for obesity [6], and from land use controls as an intervention for controlling alcohol problems, sale of unhealthy food, tobacco use and access to firearms [7]. Rodgers and colleagues [1] illustrate how the gambling arena might benefit from looking not only at problem gambling but at several dimensions of gambling participation. There have been tendencies in the alcohol arena to create artificial conceptual firewalls between addicted drinkers and the rest—fuelled in part by industry interests to promote the view that alcohol-related damage, harm and costs are the domain of the addict and are related in no way to the activities of the ‘normal’ drinker. However, it is now well established that a large share of alcohol's burden involves people who are not addicted or considered dependent [8,9]. Given that in drinking cultures the majority of adults consume alcohol, even the occasional drinking and driving incident, social disruption and other events from such a large population base will add up to a substantial number of incidents. All classifications matter, from abstainers to heavy drinkers, because on an individual basis no status is necessarily permanent, none are free from damage from alcohol (abstainers can be victims of alcohol-related harm) and all contribute to drinking culture and challenges of managing alcohol in a society. Rodgers et al.[1] appear to have a similar perspective with regard to gambling. There are many challenges in the measurement of alcohol consumption and damage from alcohol. Briefly, while studies vary, it is difficult to find survey data that produce ‘coverage’ from self-reports that account for more than 50% of sales in the region surveyed, typically less. There are also questions of validity and stability of self-report responses—and in some cases biological measures are used to corporate or detect the gap between actions and reports about them [10]. Reports on victimization or disruption due to drinking by others can also be problematic, as some of the victims may have varying criteria of what was untoward or, due to heavy drinking on their part, their awareness of an incident and recall may be hampered. Finally, the protocol and specific questions for cross-sectional surveys may vary as emerging issues and research questions change over time, thus confounding trend analysis. Similar generic challenges are evident in the gambling arena. Alcohol consumption and gambling often are concurrent activities, and it would be of interest to read more about convergence and divergence of heavy drinking and extensive gambling. Even on-line gambling is likely to include drinking and in many gambling venues, such as casinos, alcohol is omnipresent. There are likely to be several parallels between the alcohol and gambling industries. They may be corporately intertwined, or have similar approaches to minimize the association with problems and prevention controls, seeking to link all/most problems with the heaviest users, concurrent with efforts to promote the drinking or gambling opportunities and behaviours as normal. Many governments have an ongoing ambivalence to the activities due to a combination of the large revenues that they draw from alcohol use and gambling, combined with social, law enforcement and health costs related to the behaviours. In the gambling arena, some governments have set aside a small fraction of the revenue to fund relevant treatment or research [11]. This resource, while of benefit to some, is not without its challenges from a population perspective. If the fund is based on percentage of the revenue, then those individuals or agencies who are in line to benefit will benefit more if overall gambling increases, with the resulting ironic situation that treatment may be linked unwittingly to an increase in human misery. It would be of interest to examine the trajectory of responses to alcohol problems and problem gambling. A common response is to devote substantial, and often unfocused, resources to education and persuasion [12]. This may raise awareness, change perceptions and soften attitudes. However, in the drinking and driving arena, it would be challenging to imagine that a sharp decline in injury and death would have been feasible with only informational programmes. A wide range of policies, legislation, detection techniques and law enforcement protocols have contributed to a reduction in drinking and driving, reinforced by media campaigns and widespread non-governmental organization (NGO) and government support [13]. Nor can one easily imagine a decline in smoking prevalence and increase in cessation with information being the only health promotion tool or intervention [2]. In the healthy eating domain, there are some timid steps outside the tent of the politically safe and very popular persuasion and education arena, but the full range of potential interventions and policies has yet to be developed and implemented [3]. It is likely that as the damage from gambling becomes known more widely or increases in real terms, more effective control measures will eventually be undertaken. In the meantime, there are clear advantages in expanding the scope of what is tracked and measured in this area, along the lines as outlined by the authors [1]. None.

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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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.675
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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.153
GPT teacher head0.425
Teacher spread0.273 · 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; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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
Published2009
Admission routes1
Has abstractyes

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