Can Social Media cause Needed Health Care Transformation to Occur? The STRONGERR Project
Bibliographic record
Abstract
Introduction: The key cripplers of health care are: 1. Fragmented Patient chart Possible solutions: single cloud-based chart that is owned by the patient protected by the government information uploaded by a certified care provider (or they don't get paid) Maintained by a patient navigator who organizes information linked to self-care directions and tele-support clinicians 2. Disparate and rapidly changing medical treatments of variable support with evidence Why can't we integrate all guidance into one set of current recommendations so that when you put your information into the patient's EMR, guidance pops up and you follow that. Not only will that lead to consistency, you are essentially entering a patient into a clinical trial of sorts as this data can be reviewed later. 3. CME Fragmented, disparate, inconsistent. Make it a paid part of our salary making it mandatory, and consistent 4. Telemedicine Create a Provincial or State or Regional Virtual hospital that Offers 24/7, Full hospital e-consultant services. a. Tier one, e-Consultants support acute care issues.They help you decide regardless of where you are working the management and connect with a regional hospital bed registry so you can move your patient from your ED to a hospital with beds. b. Tier two, e-Consultants who support in-patient rounds virtually in rural/remote settings with hospitalists. For example, an Internist could support and monitor a regional virtual ward and do rounds with in-house hospitalists on patients across the region. C. Tier three would be the equivalent of an outpatient clinic, done virtually. Method: A STRONGERR website using Social Media tools will be created to determine if social media can be used to accelerate health care transformation to create a unified delivery system. Results: Website will be up by Dec 2022. Results April 2023. Conclusion: To be determined.
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 | 0.031 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.058 | 0.010 |
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".