Diagnosis Blog: Checking Up on Health Blogs in the Blogosphere
Bibliographic record
Abstract
OBJECTIVES: We analyzed the content and characteristics of influential health blogs and bloggers to provide a more thorough understanding of the health blogosphere than was previously available. METHODS: We identified, through a purposive-snowball approach, 951 health blogs in 2007 and 2008. All blogs were US focused and updated regularly. We described their features, topics, perspectives, and blogger demographics. RESULTS: Approximately half of the bloggers in our sample were employed in the health field. A majority were female, aged in their 30s, and highly educated. Two thirds posted at least weekly; one quarter accepted advertisements. Most blogs were established after 2004. They typically focused on bloggers' experiences with 1 disease or condition or on the personal experiences of health professionals. Half were written from a professional perspective, one third from a patient-consumer perspective, and a few from the perspective of an unpaid caregiver. CONCLUSIONS: Data collected from health blogs could be aggregated for large-scale empirical investigations. Future research should assess the quality of the information posted and identify what blog features and elements best reflect adherence to prevailing norms of conduct.
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 | 0.019 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".