Pharmacy customers’ knowledge of side effects of purchased medicines in Mexico
Bibliographic record
Abstract
OBJECTIVES: To analyse pharmacy customers' knowledge and information sources about side effects of medicines they purchased and factors associated with this knowledge. METHODS: Cross-sectional survey and semi-structured interviews with customers of 52 randomly selected community pharmacies in Morelos state, Mexico. Customers were included if they were older than 18 years, bought at least one drug either with or without medical prescription, and agreed to take part in the survey. Data were analysed using a multinomial logistic regression model. RESULTS: A total of 1445 customers buying 1946 drugs were surveyed (age 42.9 +/- 15.7 years, 56.9% female); 627 (59%) of 1055 customers who purchased prescription-only medicines (POM) did so without a prescription. Of all customers interviewed, 172 (11.9%) affirmed that the bought medicine(s) could cause harm. Only half of those (87 or 6%) were able to identify correctly at least one side effect of the purchased medicines. The majority received the information about side effects from a physician. Customers in semirural areas knew less about side effects (odds ratio: 0.26; 95% CI: 0.11-0.61; P = 0.00); whereas customers buying medicines for chronic pain, hypertension or diabetes knew more (odds ratio 2.63; 95% CI: 1.44-4.80; P = 0.00). CONCLUSION: The overall majority of customers did not know that medicines they bought could be harmful. This is particularly alarming because they frequently used POM without consulting a physician.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".