Commentary on <scp>N</scp> elson <i>et al</i> . (2015): Challenges of adopting and implementing effective alcohol policies
Notice bibliographique
Résumé
This paper examines the intersection between the potential impact of a wide range of alcohol policies and what actually happens in the contexts of policymaking and their implementation. The focus is on the 50 US states and District of Columbia (DC) 1. Their starting-point is whether or not effective policies are unpopular and popular strategies are ineffective. Drawing upon evaluations of alcohol policies, the Alcohol Policy Information System and other sources, they identified 47 policies. A Delphi panel of 10 alcohol policy experts, using a five-point Likert scale, independently rated the efficacy (ER) of these for addressing binge drinking and alcohol-impaired driving among both the general population and youth. The analysis in this paper focuses on 29 policies. Then, considering state and DC data from 1999 to 2011, they employed two measures of implementation, again using 10 policy experts: whether the state had a given policy or not, and an implementation rating (IR). They conclude that implementation of politically palatable state-level policies, such as those focusing on youth or drinking and driving, increased during the 13-year period. Less effective policies also increased in implementation, while the most effective policies did not change in their implementation. This is an important and timely contribution to the growing literature assessing alcohol policies on their potential or demonstrated efficacy 2-9. The paper includes a substantial range of policies, multiple years, numerous jurisdictions and implementation assessment—the latter was not feasible in summary evaluations focusing on multi-country international overviews 4-6. The rationale for focusing on binge drinking and drinking and driving is probably related to the scope of state-level survey data 10. Nevertheless, there are other outcomes that have been linked to effective alcohol policies. For example, Babor et al. 6 used reduction in total consumption, high-risk drinking or alcohol-related harm—social problems, chronic disease, trauma. Future research may consider additional outcome measures, chronic diseases most clearly linked with alcohol use 11—e.g. alcohol-specific International Classification of Diseases (ICD) codes, several types of cancer, cardiovascular and intestinal diseases, liver cirrhosis and trauma, in addition to drinking and driving incidents—e.g. assaults, homicide, suicide. A longer time-frame may be needed in order to accommodate lags between a policy implementation and potential impacts on some chronic diseases where alcohol is a significant contributor. Research is also needed to shed light on what legislators know about alcohol policy efficacy and how communication on this topic can be enhanced. Do they know which policies are assessed by alcohol research experts to be highly effective, marginally effective or ineffective? Does this knowledge have a significant influence on decisions to implement less effective policies, or pass on effective ones? Or are the knowledge exchange or transformation systems non-existent, inadequate or confounded, so that the sound advice on potential efficacy of specific policies is not getting to them in a timely way? Two ways forward are proposed. This might involve selecting a few jurisdictions; for example, those showing the most dramatic decline in implementing the most effective policies and those showing an increase in implementing effective policies. Along the lines of Österberg et al. 12, research might document long-term trends in overall consumption/sales, alcohol-related harm, implementation of alcohol policies and, if feasible, public opinion on alcohol policy topics. They report that after alcohol was made more available in Finland through tax changes, evidence of alcohol-related harm increased 12, 13, and the government subsequently introduced controls. The respondents to a national survey were supportive of more effective controls. This research illustrates that governments do implement more effective policies, but typically only after there have been a large number of victims from a strategy that contributed to enhanced rather than reduced harm. A second approach is to conduct some case studies of decision-making on alcohol policy issues at state legislatures 14. This might involve a combination of intensive analysis of the Legislative or Congressional Records, and key informant interviews with legislators and/or their key staff; namely, those who were in key positions to determine which alcohol policy options were considered and their outcome. This would provide further insights into what resources, pressures or issues were considered or brought to bear on the regulatory or legislative outcome. One of the most effective alcohol policies is the pricing and taxation of alcoholic beverages. Numerous studies have shown strong associations with trauma, chronic disease and social problems 15, 16. Alcohol pricing impacts total populations, youth and heavy drinkers, and impacts are greater on those who drink the most 17. Policymakers need not, and have not, waited for public opinion to be strongly supportive of this intervention before implementing it. A study of US Federal legislation on alcohol taxes 18 noted that some increases were initiated in the dead of night, not because public health advocates had made a convincing harm reduction case to the legislators but because the legislators needed to find more revenue. Nevertheless, alcohol tax increases save lives, whether the decisive rational is a public health agenda or an expedient strategy. This paper points to the ongoing challenge—also noted in the World Health Organization's (WHO) Global Alcohol Strategy 19—of finding and fostering leaders who make the bold choices. These leaders may turn out to be temporarily ahead of public opinion, but very much in touch with the pressing needs of their constituents and the most effective ways to reduce alcohol-related harm in the populations that they serve. None.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 ».