RISKY SINGLE OCCASION DRINKING RESEARCH: THE FOUR STAGES OF ENLIGHTENMENT
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».