Consumer Likelihood to Seek Information on OTC Medicines
Bibliographic record
Abstract
There is concern as to whether the public use OTC (over-the-counter) medicines with due diligence. The objective was to quantify the likelihood and extent people would seek information on OTC medicines in relation to 10 non-medicine products as a surrogate of the importance consumers place on them. Citizens of one Canadian province estimated the likelihood and extent (scale of 1 to 10) they would search for information when considering a purchase. The survey had two lists-a MIXED products list (5 OTC medicine categories and 10 non-medicine products) and an OTC MEDICINES list (15 categories). Five hundred and seventy-five surveys were obtained (response rate 19.2 percent). The average age was 63.0 years and 61.6 percent were female. The mean search likelihood for the 15 products on the MIXED list ranged from 2.2 to 7.4. There was more intention to search for information involving OTC medicines (mean = 5.0) than non-medicine products (mean = 4.1). There was a weak positive correlation in search likelihood relative to OTC medicine familiarity. This study revealed that the likelihood of searching for information prior to purchase is not particularly robust. With a plethora of information currently available to consumers, motivation to access it is what may need attention.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".