Health Justice for Health Systems: Normative Guidance for the Just Allocation of Scarce Healthcare Resources by Meso Level Agents
Bibliographic record
Abstract
Existing accounts of health justice operate one or more steps removed from the practical difficulties inherent to providing healthcare services across a territory as vast and diverse as Canada’s. This is, in part, because the philosophical examination of health justice has largely failed to appreciate an important level of decision-making between the macro level of healthcare delivery, responsible for funding, priority setting and system design, and the micro level, responsible for clinical, bed-side care. The meso level, situated between the two, is where scarce healthcare resources are allocated according to the priorities set by the macro level, to be utilized for patient care by micro level. Allocative decisions made at the meso level are, in large part, responsible for inequities of the sort that motivate this dissertation and, as such, require normative guidance, if justice is to obtain. This dissertation begins with and argument for, and defense of, a meaningful distinction between the ‘big’ and ‘smaller’ problems in the just allocation of scarce healthcare resources. A gap exists between what a publicly-funded healthcare system owes the population (e.g., as a result of legislation, or as a matter of justice) and what the healthcare system can deliver once constraints (e.g. human, financial) are considered. This is the ‘big’ problem. How a healthcare system goes about allocating scarce healthcare resources in light of that gap is a distinct, ‘smaller’ problem, that disproportionately affects rural communities. The ‘smaller’ problem is then situated at the meso level of healthcare delivery, a level that has, to date, been largely ignored by philosophers, or conflated with other levels. I proceed to show that adequate normative guidance does not yet exist for the just allocation of scarce healthcare resources by meso level actors. Finally, consideration is given to how this lack of normative guidance might be addressed. I argue that arriving at suitable normative guidance will not be achieved by simply working out details based on principles and methods contained in existing theories or approaches to health justice. The dissertation concludes with an examination of fairness contractualism as a possible means of generating normative guidance.
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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 | 0.057 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.090 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.020 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".