Medicare and the Non-Insured Health Benefits and Interim Federal Health Programs: A procedural justice analysis
Bibliographic record
Abstract
Procedural justice in health care goods and services allocation is a necessary, though likely insufficient, condition for a just health care system. Specific health care systems should accordingly be subject to procedural justice analyses. Norman Daniels and James E Sabin’s accountability for reasonableness framework is one of the best accounts of procedural justice in the health care allocation context. This framework requires the public display of decisions and the reasons for health care allocation decisions (“publicity” or “transparency”), the use of publicly accepted or acceptable rationales in those decisions (“acceptance” or “acceptability”), and mechanisms for challenging and/or appealing the decisions (“reviewability”); it may also require legal protection of the fulfillment of the first three conditions (“regulation”). These conditions provide clear metrics for assessing nations’ compliance with their framework account of procedural justice. This article accordingly applies that framework to three pillars of the Canadian health care system – Medicare, the Interim Federal Health Program, and the Non-Insured Health Benefits Program – to assess the extent to which Canada meets the demands of at least one influential account of procedural justice. It ultimately finds serious deficits in the publicity/transparency of the Canadian health care system, which makes it difficult to apply acceptability metrics, but identifies some progressive steps in better compliance with the publicity/transparency and reviewability components of the accountability for reasonableness framework. It also identifies non-drastic measures Canada can take to better achieve Daniels and Sabin’s vision of procedural justice in health care allocation.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| 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".