Bibliographic record
Abstract
The Internet is an ideal tool for promoting public health goals of prolonging life, health, and improving the quality of life. There are many websites with health-related information where one can go to as an information source, for health advice, or selfdiagnosis. However, these health websites require a more acute awareness of ethical issues due to potential life threatening risks from misuse of information. Providing disclaimers and accreditation logos only goes so far in covering potential legal conflicts, but fulfilling ethical obligations for non-maleficence requires more action on our part. As such, the content lifecycle of these websites requires greater emphasis on privacy, security, and trustworthiness. We propose and give a high-level description of a Health Content Management System (HCMS) that addresses both the managerial, as well as the ethical issues with health content. Surveys of existing health websites and content management systems demonstrate the need for the proposed system. Moreover, the novelty of the proposed HCMS is appraised and asserted in comparison with similar health framework concepts. Our contributions include survey results of more than 50 health websites, taxonomy of health websites’ characteristics, discussion about legal versus ethical obligations, and a blueprint for typical and novel features for health websites. Moreover, this study presents a new approach to analysing health content via lifecycles. Keywords-Ethics; Trust; Medical; Websites; CMS; CMF; Review; Disclaimer; Liability
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 | 0.026 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".