Attitudes Toward and Use of Over-The-Counter Medications among Teenagers: Evidence from an Italian Study
Bibliographic record
Abstract
In recent years, the consumption of Over-The-Counter (OTC) drugs has increased. Previous studies have pointed out that the OTC medications are misused and abused by teenagers, who often show poor knowledge of the toxicity of these drugs. The paper aims to analyze the use of OTCs by teenagers and the factors that influence their consumption. This paper is based on quantitative data. A web-based survey was administered to the students of an Italian high school. The questionnaire included queries on the knowledge, attitudes and practices of the students with regard to OTCs. Since teenagers are influenced by their families, their approach to healthcare was also investigated. An exploratory factor analysis was conducted in order to determine the key factors influencing their attitudes toward the use of medications. Finally, a cluster analysis was run in order to identify different behavioral segments. Results show that four factors (tradition, social communication, self-management, and caution) influence the attitudes and behaviors of teenagers toward OTC medicines. The research also highlighted that attitudes toward the use of OTC medicines varied among the respondents, who were grouped into three different clusters: their presence implies the need for targeted educational programs, involving teenagers and their families.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".