The Effectiveness of Warning Labels for Consumers: A Meta-Analytic Investigation into Their Underlying Process and Contingencies
Bibliographic record
Abstract
Although several meta-analyses have been conducted on the effectiveness of warning labels, many questions regarding their effectiveness remain unanswered. The authors identify 243 effect sizes from 66 primary articles, more than three times the number of effect sizes included in the most comprehensive meta-analysis to date. This updated and substantially larger data set shows that label effectiveness is contingent on the type of expected behavioral outcome. Labels aimed at moderation/cessation display a generally diminishing cascade of effects from attention (r = .32), comprehension (r = .37), recall (r = .31), judgment (r = .22), and behavior (r = .18). Labels targeting safe use show stronger effect sizes for behavior (r = .39) despite displaying a downward trend for attention (r = .35), comprehension (r = .29), recall (r = .32), and judgment (r = .21). The authors also find evidence of increased effectiveness when preactivating the label by means of an integrated communication strategy (r = .49). In addition, the results show the impact of several contextual factors (e.g., social influence [r = .33] and exposure frequency [r = .12]).
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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 | 0.058 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.031 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".