Health information from the web — assessing its quality: a KET intervention
Bibliographic record
Abstract
The World Wide Web has been recognized as a significant Information and Communication Technology (ICT) to educate and empower consumers by providing information on their health problems, prevention/management of diseases and related health services. It has the ability to reach those with limited access to information, potential for online support/interaction, access to volumes of information on a wide breadth of topics and the ability for people to access information when needed or ¿in real-time¿. Information professionals and healthcare providers have become aware that consumers are increasingly using the web for meeting their health information needs. However, concerns remain about the potential effects of seeking health information on the web for health matters as consumers tend to be non-clinical and may not be able to judge the quality of online health information resources. The purpose of this pilot Knowledge Exchange and Transfer (KET) project was to develop a tool (Information Brochure) that could be used by consumers to empower and to help them identify higher-quality web health information. A structured process was used for the development of the KET intervention. Consumer feedback and evaluation confirmed that the intervention can be useful to increase knowledge about their health conditions, better communications with healthcare providers and assist in making decisions about their own health.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".