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Enregistrement W3017098443 · doi:10.1111/add.15066

Commentary on Conigrave <i>et al</i> . (2020): Meta‐analysis of drinking patterns in Aboriginal and Torres Strait Islander populations highlights policy and research opportunities

2020· letter· en· W3017098443 sur OpenAlexaboutno aff
Cheneal Puljević, Helen M. Haydon, Centaine L. Snoswell

Notice bibliographique

RevueAddiction · 2020
Typeletter
Langueen
DomaineMedicine
ThématiquePrenatal Substance Exposure Effects
Établissements canadiensnon disponible
Organismes subventionnairesWellcome Trust
Mots-clésPsychological interventionIndigenousCLARITYMainstreamEnvironmental healthPoison controlSuicide preventionMedicinePsychologyNursingPolitical science

Résumé

récupéré en direct d'OpenAlex

Tailored community-driven alcohol interventions require information regarding local alcohol consumption patterns and the factors driving alcohol consumption behaviours. Future research could focus on the moderators of risky drinking patterns so that local interventions can be successfully developed. Routinely, policy in this area is informed using national statistics regarding alcohol consumption, resulting in what Conigrave and colleagues refer to as ‘broad-stroke’ interventions that are not tailored to the communities in which they are implemented. This problem is further exacerbated by the heterogeneous nature of drinking behaviours between different Indigenous Australian subgroups, as described by this meta-analysis [1]. In response, the authors call for community-controlled responses to risky drinking at a local level, which is a promising approach given evidence for improved health outcomes among Indigenous people who receive health care from Aboriginal community-controlled health services (ACCHS) rather than mainstream general practice [2]. However, it is unclear exactly how these ACCHS, or other organizations, could deliver interventions reducing risky drinking behaviours. This lack of clarity regarding how to achieve this is not unique to this article [3, 4]. It does, however, highlight a clear need for further research on the ‘solution’ in the future, rather than further quantification of the ‘problem’ of high levels of risky alcohol use. For example, a randomized controlled trial currently under way, which involves several of the authors of this meta-analysis [5], is addressing this gap by investigating the effectiveness of a model of tailored and collaborative support for ACCHS in increasing use of alcohol use screening and treatment provision. Conigrave and colleagues [1] also highlight the often damaging sporadic drinking patterns in ‘dry’ communities propagated by current policy aimed at encouraging abstinence. Future research could examine these patterns more fully. For instance, studies could explore how community-driven social marketing approaches may impact upon such episodic patterns. These approaches employ the use of marketing concepts with the purpose of eliciting positive behaviour changes among their target audiences [6]. Social marketing, which has been used effectively to promote positive life-style changes in Canadian First Nation's communities, is underutilized in Australian Indigenous communities [7], despite evidence for its effectiveness in promoting healthy behaviour changes [7, 8], including reductions in risky drinking behaviours [6]. While this approach does not take into account the social determinants that potentially drive risky alcohol use among Australian Indigenous people, it may have the potential to be a pragmatic solution allowing adaption to suit the needs of individual communities. Conigrave and colleagues also highlighted the influence of moderators such as gender and age on drinking patterns among Australian Indigenous people, reflecting the findings of a number of other studies conducted among non-Indigenous populations [9, 10]. We agree with the authors that future research examining these moderating factors, especially from a socio-cultural perspective, is critical. It should be coupled with research into other moderating factors, such as socio-economic status, which was poorly reported in the studies reviewed [1], and past experience of trauma or violence, which has a known impact but was not examined [11]. Furthermore, as many studies in this area have traditionally been guided by western paradigms [11], there is clearly an urgent need for the involvement of Indigenous researchers and Elders in the design and implementation of future studies, and for the culturally appropriate adaptation of instruments measuring alcohol consumption for an Indigenous context [1]. In conclusion, the meta-analysis by Conigrave et al. [1] highlights a number of critical directions for policy and research policy, including strengthening the evidence base for effective interventions targeting risky drinking patterns (especially by ACCHS) and the crucial need for future research to consider the benefits of a social marketing approach, and the roles of socio-economic status and a broader range of other moderating factors on risky drinking patterns among Australian Indigenous populations. None. Cheneal Puljević: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; resources; software; supervision; validation; visualization. Helen Haydon: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; resources; software; supervision; validation; visualization. Centaine Snoswell: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; resources; software; supervision; validation; visualization.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,512
Score d'incertitude au seuil0,955

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,099
Tête enseignante GPT0,375
Écart entre enseignants0,276 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2020
Routes d'admission1
Résumé présentoui

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