Evaluation of Web-Based Consumer Medication Information: Content and Usability of 4 Australian Websites
Bibliographic record
Abstract
BACKGROUND: Medication is the most common intervention in health care, and written medication information can affect consumers' medication-related behavior. Research has shown that a large proportion of Australians search for medication information on the Internet. OBJECTIVE: To evaluate the medication information content, based on consumer medication information needs, and usability of 4 Australian health websites: Better Health Channel, myDr, healthdirect, and NPS MedicineWise . METHODS: To assess website content, the most common consumer medication information needs were identified using (1) medication queries to the healthdirect helpline (a telephone helpline available across most of Australia) and (2) the most frequently used medications in Australia. The most frequently used medications were extracted from Australian government statistics on use of subsidized medicines in the community and the National Census of Medicines Use. Each website was assessed to determine whether it covered or partially covered information and advice about these medications. To assess website usability, 16 consumers participated in user testing wherein they were required to locate 2 pieces of medication information on each website. Brief semistructured interviews were also conducted with participants to gauge their opinions of the websites. RESULTS: Information on prescription medication was more comprehensively covered on all websites (3 of 4 websites covered 100% of information) than nonprescription medication (websites covered 0%-67% of information). Most websites relied on consumer medicines information leaflets to convey prescription medication information to consumers. Information about prescription medication classes was less comprehensive, with no website providing all information examined about antibiotics and antidepressants. Participants (n=16) were able to locate medication information on websites in most cases (accuracy ranged from 84% to 91%). However, a number of usability issues relating to website navigation and information display were identified. For example, websites not allowing combinations of search terms to be entered in search boxes and continuous blocks of text without subheadings. CONCLUSIONS: Of the 4 Australian health information websites tested, none provided consumers with comprehensive medication information on both prescription and nonprescription medications in a user-friendly way. Using data on consumer information needs and user testing to guide medication information content and website design is a useful approach to inform consumer website development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.074 | 0.082 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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; both teacher heads agree on what is shown here.
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".