The Need for Medical Professionals to Join Patients in the Online Health Social Media Discourse
Bibliographic record
Abstract
Health social media is frequently used by e-patients for seeking health information online for self-diagnosis, self-treatment, and self-education Health social media also provides various benefits for patients and laypersons, such as allowing users to be part of virtual support groups, having quick access to advice, and the convenience of access via the Internet At the same time, it raises concerns about misinformation being propagated by laypersons without professional medical expertise, especially during pandemics like COVID-19, leading to an infodemic There are only a handful of health social media websites that allow medical professionals to participate in discussions with patients online We postulate that the modern face of medicine and healthcare needs medical professionals to be included in the online patient discourse so misinformation can be addressed early on and head on To this end, we propose a new and free health social network named Cardea that is under development which aims to bring patients, laypersons, and medical professionals together on the world wide web Users can share experiences, ask questions, and get answers in three streamlined environments: Patient to Patient (P2P), Patient to Medic (P2M), and Medic to Medic (M2M) While there are several forums that cater specifically to patient-patient discussions or medic-medic connections, Cardea’s added value is in providing a unified portal for both patients and medics, as well as enabling interactions between patients and medics Moreover, Cardea applies machine learning, information retrieval, and natural language processing methods to promote credible health information and demote misinformation At the same time, with enhanced veracity, anonymity, and privacy controls, the vision of Cardea is for e-patients to confidently share experiences and opinions without being stigmatized or compromising their right to privacy Our hope is to generate discussion and gather insights from other researchers on developing Cardea Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda All rights reserved
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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.001 | 0.020 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".