Information Flow and Health Policy Literacy: The Role of the Media
Bibliographic record
Abstract
People increasingly can and want to obtain and generate health information themselves. With the increasing do-it-yourself sentiment comes also the desire to be more involved in one’s health care decisions. Patient driven health-care and health research models are emerging; terms such as participatory medicine and quantified-self are visible increasingly. Given the health consumer’s desire to be more involved in health data generation and health care decision making processes the authors submit that it is important to be health policy literate, to understanding how health policies are developed, what themes are discussed among health policy researchers and policy makers, to understand how ones demands would be discussed within health policy discourses. The public increasingly obtains their knowledge through the internet by searching web browsers for keywords. Question is whether the “health consumer” to come has knowledge of key terms defining key health policy discourses which would enable them to perform targeted searches for health policy literature relevant to their situation. The authors found that key health policy terms are virtually absent from printed and online news media which begs the question how the “health consumer” might learn about key health policy terms needed for web based searches that would allow the “health consumer” to access health policy discourses relevant to them.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.012 |
| 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".