Framing the Risk of an OTC Medication Side Effect
Bibliographic record
Abstract
Background: Finding the best way to communicate risk of side effects to patients can be difficult for pharmacists. Some practitioners are reluctant to discuss side effects for fear that such information may discourage medication use; for others, the number of side effects to mention is an inexact science. When actually discussing a side effect, should pharmacists phrase the chances of occurrence as “Most people don't experience X” or as “A few people do experience X”? Literature on decision-making suggests that the interpretation of information varies depending on the presentation format or the frame used. Objective: To examine the impact 2 different ways of phrasing the occurrence of a side effect has on the likelihood of a patient taking a medication. Methods: Volunteers were presented with hypothetical scenarios. They indicated their likelihood of taking 2 fictitious over-the-counter headache medications, each while considering the chance of experiencing a side effect (heartburn). The likelihood of experiencing the side effect was the same for each situation, but was presented in 2 different ways (positive or negative frame). Interviews were then carried out to gain insight into the reasons for choices made. Results: Thirty subjects participated; most were female. Participants were more likely to take a medication when the side effect was framed positively. Gender and recent history of experiencing a side effect did not appear to affect the results. Conclusion: When considering one side effect, framing the risk of its occurrence in a positive way increased the likelihood that a person would decide in favour of taking the medication.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".