Health Care Delivery in America: Its weaknesses and how to make it better
Bibliographic record
Abstract
Event Description Health care in America is an unmortared mosaic of services and funding sources. We are the only developed country without a mandatory system and our aggregate costs absorb 18% of our GDP and is as much as double what other countries are spending. Insurance plays a big role as a source of payment and dysfunction. The lecture will provide an overview of all programs which regulate and fund health care ranging from Medicare to Obamacare and private insurance and all the sources of delivery of services, from hospitals to Community Health Centers. Variations in state programs ranging from Mississippi to Massachusetts will be considered. Our system will be compared to variations on mandated and universal systems deployed in other countries including the UK, Canada, Israel. It will raise the question whether health care is a primary right or an entitlement. Finally, it will consider the prospects for improvement including eight incremental changes. Speaker Bio Sheldon Greene is the author of eight well received published novels as well as articles published in scholarly journals. He was appointed Warden of Insurance of the State of Ohio at age 23. A public interest lawyer, Greene pursued seminal issues literally decades before they achieved national attention, such as our flawed health delivery system, the impact of illegal immigration on the economy, renewable energy, and our public land policies. Greene was a participant in the first Obama national policy team for both immigration and energy. He was one of the founders of the New Israel Fund and helped formulate its unique structure drawing on his experience as General Counsel of California Rural Legal Assistance. He is an executive in a wind energy development company and has been actively engaged in renewable energy for over 25 years. He served on the Advisory Board of the Great Lakes Energy Institute at Case Western Reserve University School of Engineering for 10 years. He advocates the formulation of a God concept derived from the life process, stripped of anachronistic anthropomorphic characteristics. He has developed personal guidelines for a balanced, seamless life with spiritual, intellectual, social, and creative dimensions.
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 | 0.033 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.013 | 0.029 |
| Insufficient payload (model declined to judge) | 0.013 | 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".