Does the packaging of health information affect the assessment of its reliability? A randomized controlled trial protocol
Bibliographic record
Abstract
Background Wikipedia is frequently used as a source of health information. However, the quality of its content varies widely across articles. The DISCERN tool is a brief questionnaire developed in 1996 by the Division of Public Health and Primary Health Care of the Institute of Health Sciences of the University of Oxford. They claim it provides users with a valid and reliable way of assessing the quality of written information. However, the DISCERN instrument’s reliability in measuring the quality of online health information, particularly whether or not its scores are affected by reader biases about specific publication sources, has not yet been explored. Methods This study is a double-blind randomized assessment of a Wikipedia article versus a BMJ literature review using a modified version of the DISCERN tool. Participants will include physicians and medical residents from four university campuses in Ontario and British Columbia and will be randomized into one of four study arms. Inferential statistics tests (paired t-test, multi-level ordinal regression, and one-way ANOVA) will be conducted with the data collected from the study. Outcomes The primary outcome of this study will be to determine whether a statistically significant difference in DISCERN scores exists, which could suggest whether or not how health information is packaged influences how it is assessed for quality. Plain Language Summary The internet, and in particular Wikipedia, is an important way for professionals, students and the public to obtain health information. For this reason, the DISCERN tool was developed in 1996 to help users assess the quality of the health information they find. The ability of DISCERN to measure the quality of online health information has been supported with research, but the role of bias has not necessarily been accounted for. Does how the information is packaged influence how the information itself is evaluated? This study will compare the scores assigned to articles in their original format to the same articles in a modified format in order to determine whether the DISCERN tool is able to overcome bias. A significant difference in ratings between original and inverted articles will suggest that the DISCERN tool lacks the ability to overcome bias related to how health information is packaged.
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 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 | 0.021 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".