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Enregistrement W1597091882 · doi:10.1111/j.1360-0443.2012.03773.x

OVERESTIMATION OF PEER SUBSTANCE USE: ADDITIONAL PERSPECTIVES

2012· article· en· W1597091882 sur OpenAlexaboutno aff
Brian Borsari, Kate B. Carey

Notice bibliographique

RevueAddiction · 2012
Typearticle
Langueen
DomainePsychology
ThématiqueBehavioral Health and Interventions
Établissements canadiensnon disponible
Organismes subventionnairesNational Institute on Alcohol Abuse and Alcoholism
Mots-clésSocial norms approachPsychologyNormativeSocial psychologyContext (archaeology)RespondentRelevance (law)PerceptionOperationalizationIgnoranceEpistemologyPolitical science

Résumé

récupéré en direct d'OpenAlex

We read Dr Pape's manuscript with interest, because it questions the large body of research that demonstrates that individuals tend to overestimate peer substance use (descriptive norms). Dr Pape asserts that methodological limitations in the current norms literature call into question the notion that young people overestimate peer alcohol and drug use norms [1]. Our concern is that the reader exposed only to her paper would come away with an incomplete understanding of current knowledge about perceived substance use norms. Specifically, the review fails to consider much of the literature informing our understanding of norms perception, including papers of direct relevance to points she makes, and does not address the conceptual complexity emergent within the norms literature. We were puzzled by the omission of studies on injunctive norms, defined as the peer approval of substance use. This decision has potential implications for her central thesis, as Borsari & Carey [2] established that self–other discrepancies were highest in the context of injunctive norms relative to descriptive norms. Given a narrow focus on descriptive normative perceptions, Dr Pape raises legitimate concerns about accuracy of self-reports under some conditions and potential bias in measuring perceived norms. However, if misperceptions of norms were due solely to measurement bias, intentional under-reporting, respondent ignorance or random error, one might expect the findings to be more inconsistent. Thus, even with the methodological limitations of extant research, how does one explain the consistent pattern of overestimation of peer substance use? Notably absent from this review is the recognition that international studies suggest that the phenomenon of exaggerated substance use norms is highly generalizable. Consistent findings from Canada [3], New Zealand [4], Scotland [5] and France [6], as well as from the Scandinavian studies cited, reveal a similar pattern of misperception that holds up across multiple cultures, operational definitions and varying sampling strategies [4,7]. More troubling is the omission of literature that has addressed explicitly many of the proposed directions of future research. For example, ‘how the commonly used term “the typical student” is perceived’ (p. 13) has been addressed; the typical college student is indeed perceived as male [8], and enhanced relevance is achieved by assessing perceived norms for referent groups at least one step closer to the respondent than the typical college student, matching to gender, local college or affiliation group [9]. Similarly, the author suggests linking perceptions of targets with the targets' actual use (p. 13). Indeed, this has been employed in real-time group demonstrations of misperceived norms [10]. We suggest that the norms literature represents a great deal more methodological and conceptual complexity than is reflected in Dr Pape's review. She makes a good point about how reliance on mean differences to summarize data can mask patterns of over- and underestimation. However, the literature already recognizes lack of uniformity in perceived–actual discrepancies. For example, Kypri & Langley [4] considered responses within ±10% accurate; nevertheless, 80% of women and 73% of men overestimated prevalence of heavy drinking among peers. As cited in the review, Franca et al. [6] noted a predominance of underestimation of any use; however, a majority overestimated heavy alcohol use, suggesting that exaggerated norms exist for riskier drinking indices. We know that the magnitude of misperceptions increases in heavier users of both alcohol [11] and marijuana [12]. Logically, in heterogeneous samples such individual differences will lead to some accurate estimation, as well as under- and overestimation. On average, however, the norm is overestimated and the variability in estimates does not discredit the overall pattern but is to be understood. In conclusion, Dr Pape raises interesting methodological questions that can be addressed in future research, but has not made the case convincingly that the misperception phenomenon is exaggerated. Her comment regarding the complexity and effort required to make accurate estimates of perceived norms, especially that of typical students or other target groups, captures the key source of the influence of perceived norms. When actual data are unavailable, individuals will estimate using the information available to them. Of course, these estimates are often subject to bias, but this inaccuracy is an unavoidable consequence of how humans make sense out of complex and varied sources of data. Normative perceptions do influence personal decisions regarding alcohol and other substances. When peer norms are elevated, accurately or not, risky substance use can ensue. Therefore, the provision of credible data that fosters an intrinsic process of questioning and adjusting these misperceptions downwards can be a powerful clinical tool. Indeed, the existence of a misperception may be more important than its magnitude, and the correction of exaggerated norms can prompt more deliberate thought and decisions about substance use. This work was supported by National Institute on Alcohol Abuse and Alcoholism Grant R01 AA017874 to B. Borsari and R01-AA012518 to K. B. Carey. The contents of this manuscript do not represent the views of the Department of Veterans Affairs or the United States Government. 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 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 candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,907
Score d'incertitude au seuil0,952

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0490,000

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 Opus0,084
Tête enseignante GPT0,394
Écart entre enseignants0,310 · 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.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations6
Publié2012
Routes d'admission1
Résumé présentoui

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