Setting Limits in Healthcare: The Ontario Drug Benefit Program and Accountability for Reasonableness
Bibliographic record
Abstract
Moderate scarcity is a basic social condition and resource constraints make limit setting in healthcare inevitable. Limits are already being set at many different levels in Canadian healthcare, but limit setting often proceeds in an uncoordinated and opaque manner, with little public knowledge or involvement. The need to set limits in healthcare raises important questions about distributive justice. Substantive approaches to distributive justice are subject to significant problems, and there is no theoretical consensus at the level of ethical theory. Procedural approaches are also subject to serious flaws, but in this thesis I argue that a procedural approach is currently the most appropriate way to deal with morally controversial aspects of limit setting. To illustrate principles of procedural justice and limit setting in healthcare I will use the Ontario Drug Benefit Program (ODB) as a case study. The ODB provides publicly funded prescription drug coverage for vulnerable groups in Ontario, but some drugs are not listed on the Provincial formulary. The ODB sets limits on what drugs are covered, but listing decisions can be morally controversial. "Accountability for Reasonableness" (A4R) is the leading ethical framework for limit setting in healthcare, and I provide a critical assessment of the ODB by applying this framework to the way in which these limits are set. The ODB meets some of the conditions of A4R, but there is significant room for improvement. I offer five recommendations to enhance the legitimacy and fairness of ODB limit setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".