Investigating biomedical research literature in the blogosphere: a case study of diabetes and glycated hemoglobin (HbA1c)
Bibliographic record
Abstract
OBJECTIVE: The research investigated the relationship between biomedical literature and blogosphere discussions about diabetes in order to explore the role of Web 2.0 technologies in disseminating health information. Are blogs that cite biomedical literature perceived as more trustworthy in the blogosphere, as measured by their popularity and interconnections with other blogs? METHODS: Web mining, social network analysis, and content analysis were used to analyze a large sample of blogs to determine how often biomedical literature is referenced in blogs on diabetes and how these blogs interconnect with others in the health blogosphere. RESULTS: Approximately 10% of the 3,005 blogs analyzed cite at least 1 article from the dataset of 2,246 articles. The most influential blogs, as measured by in-links, are written by diabetes patients and tend not to cite biomedical literature. In general, blogs that do not cite biomedical literature tend not to link to blogs that do. CONCLUSIONS: There is a large communication gap between health professional and personal diabetes blogs. Personal blogs do not tend to link to blogs by health professionals. Diabetes patients may be turning to the blogosphere for reasons other than authoritative information. They may be seeking emotional support and exchange of personal stories.
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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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".