Alcohol consumption among young consumers: a review and recommendations
Bibliographic record
Abstract
Purpose This paper aims to examine social marketing programs aimed at preventing or moderating alcohol consumption among young consumers. It seeks to show how protection motivation theory can be used as a theoretical framework to create effective communications targeting young people. Design/methodology/approach Communication materials aimed at preventing or moderating alcohol consumption among young people were identified and gathered from web sites in five English‐speaking countries (USA, Canada, Australia, New Zealand, and the UK). A qualitative review of these materials was conducted. Findings A majority of the alcohol moderation/prevention campaigns targeting young consumers followed the tenets of protection motivation theory by focusing on the threat variables of severity and vulnerability. Some campaigns also focused on costs, as well as self‐efficacy and response efficacy. Research limitations/implications Only English‐language materials and materials targeting young consumers have been considered, so findings cannot necessarily be generalized to other languages or countries. Practical implications Future youth alcohol moderation/prevention initiatives should include self‐efficacy messages, to increase confidence among young people that they are able to carry out the recommended actions. Originality/value The review presents a comprehensive examination of initiatives aimed at preventing/reducing alcohol consumption among young consumers, and shows how protection motivation theory can be successfully used in this context.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".