Generic medicines: an evaluation of the accuracy and accessibility of information available on the internet
Bibliographic record
Abstract
BACKGROUND: Internationally, generic medicines are increasingly seen as a key strategy to reduce healthcare expenditure, therefore awareness and knowledge transfer regarding generic medicines are valid areas of research. Although the Internet is a frequently used source of medical information, the accuracy of material found online is variable. The aim of this study was to evaluate information provided on the Internet regarding generic medicines in terms of quality of information and readability. METHODS: Internet searches for information regarding generic medicine were completed, with a pre-defined search term, using the Google search engine, in five English-speaking geographical regions (US, UK, Ireland, Canada and Australia). Search results likely to be looked at by a searcher were collated and assessed for the quality of generic medicine-related information in the websites, using a novel customised Website Quality Assessment (WQA) tool; and for readability, using existing methods. The reproducibility of the tools between two independent reviewers was evaluated and correlations between WQA score, readability statistics and Google search engine results page ranking were assessed. RESULTS: Wikipedia was the highest-ranking search result in 100% of searches performed. Considerable variability of search results returned between different geographical regions was observed, including that websites identified in the Australian search generated the highest number of country specific websites; searches performed using computers with Irish, British, American and Canadian IP addresses appear to be more similar to each other than the google.com search performed in Australia; and the Canadian google.ca results show a notable difference from any of the other searches. Of the 24 websites assessed, none scored a perfect WQA score. Notably, strong correlation was seen between WQA and readability scores and ranking on google.com search results. CONCLUSIONS: This novel evaluation of websites providing information on generic medicines showed that, of the websites likely to be seen by a searcher, none demonstrated a combination of scoring highly on quality of information (as evinced by WQA score) and readability. Therefore, there is a gap in online knowledge provision on this topic which, if filled by a website designed using the WQA tool developed in this study, has an improved likelihood of ranking highly in google.com search results.
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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 | 0.024 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".