Single-Payer, Multiple-Payer, and State-Based Financing of Health Care: Introduction to the Special Section
Bibliographic record
Abstract
Single-Payer, Multiple-Payer, and State-Based Financing of Health Care: Introduction to the Special Section Peter D. Donnelly MD, Paul C. Erwin MD, DrPH, Daniel M. Fox PhD, and Colleen Grogan PhD Affiliation Peter D. Donnelly is with the Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada. Paul C. Erwin is with the School of Public Health, University of Alabama at Birmingham. Daniel M. Fox is with the Milbank Memorial Fund, New York, NY. Colleen Grogan is with the School of Social Service Administration, University of Chicago, Chicago, IL.CopyRightCorrespondence should be sent to Paul C. Erwin, MD, DrPH, Dean and Professor, School of Public Health, University of Alabama at Birmingham, 1665 University Blvd, RPHB 140B, Birmingham, AL 35294-0022 (e-mail: perwin@uab.edu). Reprints can be ordered at http://www.ajph.org by clicking the "Reprints" link.CONTRIBUTORSAll authors were involved in concept development, writing, reviewing, and finalizing of the editorial. https://doi.org/10.2105/AJPH.2019.305353 Accepted: August 15, 2019 Published Online: October 02, 2019
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".