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OVERESTIMATION OF PEER SUBSTANCE USE: ADDITIONAL PERSPECTIVES

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

Bibliographic record

VenueAddiction · 2012
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsnot available
FundersNational Institute on Alcohol Abuse and Alcoholism
KeywordsSocial norms approachPsychologyNormativeSocial psychologyContext (archaeology)RespondentRelevance (law)PerceptionOperationalizationIgnoranceEpistemologyPolitical science

Abstract

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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.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.046
metaresearch head score (Gemma)0.179
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.046
Threshold uncertainty score0.246

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.179
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0020.007
Scholarly communication0.0070.015
Open science0.0030.004
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0090.001

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 head0.084
GPT teacher head0.394
Teacher spread0.310 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2012
Admission routes1
Has abstractyes

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