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Record W2793236249 · doi:10.1111/add.14121

Commentary on Pickering <i>et al</i>. (2018): Problems with measurement consistency—unique to gambling or widespread in addictions?

2018· letter· en· W2793236249 on OpenAlexaffabout
Nancy M. Petry, David C. Hodgins

Bibliographic record

VenueAddiction · 2018
Typeletter
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsUniversity of Calgary
FundersNational Institute on Alcohol Abuse and AlcoholismNational Institutes of Health
KeywordsPsychologyAddictionGambling disorderConsistency (knowledge bases)Substance abuseClinical psychologyDistressConstruct validityPsychiatryPsychometrics

Abstract

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Few studies of gambling treatment outcomes have used consistent indices of gambling and gambling-related problems. Problems with consistency of measurement also apply to studies of substance abuse treatment. Research and clinical practice across the addictions could benefit from greater inclusion of objective, reliable and valid indices of behaviors. More than a decade ago, Walker et al. 1 identified the need for consistency in measuring gambling outcomes. In a systematic review, Pickering et al. 2 concluded that treatment outcomes for gambling disorder remain defined poorly and assessed inconsistently. Few studies captured variables identified within the Walker et al. 1 consensus statement. On one hand, we could consider the lack of appropriate gambling outcomes as a failure of researchers to produce rigorous and reproducible results. On the other hand, substance use disorder treatment has also suffered from similar concerns, and cross-discipline comparisons may guide future efforts across addictions. In studies that Pickering et al. 2 reviewed, assessment of non-gambling constructs was in many ways better than assessment of gambling-related constructs. Indices administered to evaluate psychological distress and symptoms, for example, are used widely and have substantial evidence of reliability and validity. In contrast, the gambling instrument most often applied, the South Oaks Gambling Screen, is known to overestimate symptoms and classification status 3-5. Only 10 studies applied the DSM criteria for gambling disorder following treatment, and even DSM-based instruments have not undergone extensive psychometric testing. Gambling instruments in general have limited information related to reliability and validity, limited most often to construct validity and internal consistency 6. As Pickering et al. 2 point out, measurement inconsistencies impact the ability to compare across studies. They also mitigate against drawing firm conclusions regarding efficacy of interventions within a study. For example, a study may report upon reductions in a composite score, but if that index is comprised of items that do not relate reliably or validity to changes in gambling problems, the outcome may have little clinical relevance. Cognitions are more challenging to quantify reliably and validly than behaviors, yet gambling studies often report on cognitive, rather than behavioral, outcomes or use instruments that confound the two. Of studies that Pickering et al. 2 reviewed, only approximately half presented data specifically on gambling behaviors, and fewer than a third evaluated clinical harms based on diagnostic criteria. Using somewhat broader criteria, we found a higher proportion of studies included a behavior index alone or in conjunction with clinical harms 7. In the gambling field we must rely upon self-report, and Walker et al. 1 recommended using collaterals (e.g. family and friends) to validate participant reports of gambling behavior, which has been performed in a number of studies. We would argue that assessing changes in gambling behavior using the most rigorous methods possible is necessary, and standardization of methodologies would, of course, improve comparability across studies. Although few substance abuse treatment studies rely primarily or exclusively on cognitive outcomes, those fields suffer similarly from concerns with respect to reporting outcomes. Alcohol use disorders are similar to gambling, in that no objective index is readily available, both are legal activities and moderate engagement can occur without adverse consequences. The alcohol field has long struggled with quantifying outcomes, and extensive research led to current frequency–quantity guidelines that relate to harmful use, i.e. > 14 drinks/week for men (more than seven for women) for hazardous drinking and more than four drinks/occasion for men (more than three for women) for binge drinking in the United States 8. Most alcohol treatment studies now rely upon self-reported outcomes that assess these behavioral constructs along with proportion of non-drinking days, but decades of extensive cross-cultural research guided their development. For illicit drug use, biological markers exist making objective quantification of recent use possible, but they are rarely applied clinically and are absent from many clinical trials. As in the medical field more generally, objective indices should be applied whenever available; no study or clinical visit for heart disease, for example, would fail to include assessment of blood pressure each time the patient presented. Addictions in general are distinct from mainstream medicine with respect to treatment and research funding, and part of this segregation may reflect and/or stem from inabilities to assess symptoms in a standardized manner. The Pickering et al. 2 report confirms that the gambling field has a long way to go. The alcohol and drug use fields can provide guidance on the types of data and methods needed to establish better outcomes, and some substance use measures may be directly relevant to non-substance addictions, obviating the need to develop unique instruments. The substance use field could benefit similarly from a similar review of its outcome measures and their abilities to classify harms. Conscientious efforts to improve assessments of outcomes are necessary to improve research as well as clinical treatment across all addictions. N.M.P. has received consulting fees for preparing a review for the Responsible Gambling Trust. D.C.H. has received travel funding and speaker fees from Gambling Research Exchange Ontario (GREO) and conference travel funds from the National Association for Gambling Studies and conference speaker funds from the European Association for the Study of Gambling. He serves on the Scientific Advisory Board of the National Center for Responsible Gaming, but receives no remuneration. He receives partial salary support from the Alberta Gambling Research Institute, which is funded by the province of Alberta.

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)0.001
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
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)0.0010.001

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 head0.147
GPT teacher head0.359
Teacher spread0.212 · 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
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

Citations0
Published2018
Admission routes2
Has abstractyes

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