On Measuring the Inequity of Financing Health Care in the United States and the Redistribution of Income Through Health Care Financing in Canada
Bibliographic record
Abstract
On Measuring the Inequity of Financing Health Care in the United States and the Redistribution of Income Through Health Care Financing in Canada Michel L. Grignon PhD, Sara Allin PhD, Lisa Corscadden MPH, and Michael Wolfson PhD Affiliation Michel L. Grignon is with the Department of Economics and the Department of Health, Aging & Society, McMaster University, Hamilton, ON. Sara Allin is with the Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON. Lisa Corscadden is with the Australian Institute of Tropical Health and Medicine, James Cook University, Australia. Michael Wolfson is with the Faculty of Medicine, School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, ON.CopyRightCorrespondence should be sent to Michel Grignon, Professor, Department of Economics, McMaster University, KTH 426, 1280 Main St West, Hamilton, ON L8S 4M4, Canada (e-mail: grignon@mcmaster.ca). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link.CONTRIBUTORSM. L. Grignon wrote the first draft of the editorial. All authors edited and discussed the editorial to arrive at a final version. https://doi.org/10.2105/AJPH.2020.305891 Accepted: July 23, 2020 Published Online: October 07, 2020
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 | 0.022 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".