POLICY AND PREVENTION Normative Misperceptions about Alcohol Use in a General Population Sample of Problem Drinkers from a Large Metropolitan City
Bibliographic record
Abstract
Abstract — Aims: Heavy drinkers tend to overestimate how much others drink (normative fallacy), at least in college samples. Little research has been conducted to evaluate whether normative misperceptions about drinking extend beyond the college popu-lation. The present study explored normative misperceptions in an adult general population sample of drinkers. Methods: As part of a larger study, in Toronto, Canada, a random digit dialling telephone survey was conducted with 14,009 participants who drank alcohol at least once per month. Respondents with Alcohol Use Disorders Identification Test of eight or more (n = 2757) were asked to estimate what percent of Canadians of their same sex: (a) drank more than they do; (b) were abstinent and (c) drank seven or more drinks per week. Respondents ’ estimates of these population drinking norms were then compared with the actual levels of alcohol consumption in the Canadian population. Results: A substantial level of normative misperception was observed for estimates of levels of drinking in the general population. Estimates of the proportion of Canadians who were abstinent were fairly accurate. There was some evidence of a positive relationship between the respondents ’ own drinking severity and the extent of normative mis-perceptions. Little evidence was found of a relationship between degree of normative misperceptions and age. Conclusion: Normative misperceptions have been successfully targeted in social norms media campaigns as well as in personalized feedback interventions for problem drinkers. The present research solidifies the empirical bases for extending these interventions more widely into the general population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".