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RISKY SINGLE OCCASION DRINKING RESEARCH: THE FOUR STAGES OF ENLIGHTENMENT

2011· article· en· W1497802288 on OpenAlexaff
Gerhard Gmel, Emmanuel Kuntsche, Jürgen Rehm

Bibliographic record

VenueAddiction · 2011
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsToronto Public Health
Fundersnot available
KeywordsOperationalizationBinge drinkingNormativePsychologyPublic healthSocial psychologyConfusionPoison controlHuman factors and ergonomicsMedicineEnvironmental healthPolitical scienceEpistemologyLaw

Abstract

fetched live from OpenAlex

We are grateful for the comments of our highly esteemed colleagues. First, they raise concerns about the name and definition of the underlying concept [1,2]. While the sociology of research of binge drinking is interesting [2], our aim was much more modest: we have been trying to clarify a concept which could help research to better interpret the myriad of empirical findings. With this objective in mind and to avoid confusion, we prefer the term risky single occasion drinking (RSOD) in order to separate it from more chronic heavy drinking. Second, to define the cut-off for RSOD may be difficult in terms of a fixed number of drinks (e.g. five drinks or more) due to different concentrations of ethanol of drinks across countries. Our focus was not on promoting a particular cut-off as may have been understood by Dr Chikritzhs [3]; instead, we tried to review the literature by focusing on articles where single occasion alcohol consumption was between 60–70 grams for men, and 40–60 grams for women. Cut-offs for research must be defined based on the research question and the scientific evidence, and should not be confused at all with normative cut-offs such as those used in drinking guidelines, for example. Our aim was not to translate research into understandable and evidence-based public health guidelines or measures, but to raise awareness for research needed to feed into correct public health messages. In terms of measurement via blood alcohol concentrations [1,2]: although such an operationalization is feasible in some studies, it seems to be difficult for the majority of studies in alcohol epidemiology, which have to rely on self-report, if only for budgetary considerations. Third, we agree there is a risk of problem amplifying of RSOD [2], which could lead to stigmatizing some behavior. For instance, problem amplifying may occur in longitudinal research where a baseline measure of RSOD is used as a predictor for chronic disease including alcohol use disorders without analyzing whether a ‘youth pattern’ with RSOD on some weekends has changed to a more chronic heavy drinking pattern. Problem amplifying may also happen when the frequency of RSOD is dichotomized into yes/no, and therefore chronic diseases are ascribed to any RSOD although the link may go via chronic heavy drinking. Fourth, although we tried to describe the need for separating effects of RSOD from chronic heavy drinking including the analysis of the interplay between volume and RSOD, we had to fall short of describing the technicalities [1,4]. There still does not seem to be enough research to recommend a best practice for such modeling. Dr Dawson in her comment [4] mentioned the complexity of the contribution of RSOD at different levels of volume and pointed to approaches not included [5]. We applaud such efforts, and wish that more researchers would try to jointly consider volume and RSOD in models that go beyond simply putting both measures as main effects into one model without modeling the complex interactions. Dawson's work [5] is certainly an important step forward but many questions remain. To give but one example: would her conclusions be the same if non-standard interactions (i.e. having the same shape across all levels of volume and RSOD) are modeled? In sum: while our review could not solve all the problems and open questions, we hope to have raised awareness that bingeing is not bingeing, and alcohol epidemiology has to improve our handling of the multidimensionality of exposure. We thank Ben Taylor for English editing.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.348

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.190
GPT teacher head0.332
Teacher spread0.142 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2011
Admission routes1
Has abstractyes

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