Challenges in striving to simultaneously achieve multiple resource allocation goals: the pan-Canadian Oncology Drug Review (pCODR) example
Bibliographic record
Abstract
The pan-Canadian Oncology Drug Review (pCODR) makes recommendations to Canada's provinces and territories (except Quebec) to guide their cancer drug funding decisions. The objective of this paper is to explore, using an economic perspective and the pCODR as an example, the challenges associated with striving to simultaneously achieve the goals of maximizing health benefits with available resources and improving access to a more consistent standard of care across Canada. The first challenge concerns how to interpret the goals in order to determine how resources should be allocated to achieve each goal. The second challenge relates to whether, if pursued simultaneously, both goals can be achieved to the same extent that each goal could have been achieved alone with the same available resources. Regarding the first challenge, we illustrate that, due to a lack of definitional clarity, it is difficult to determine exactly how resources should be allocated in order to achieve the goal of improving access to a more consistent standard of care across Canada. Regarding the second challenge, we illustrate that choosing to strive for both of the pCODR goals simultaneously will likely be associated with tradeoffs in the extent to which one or both goals can be achieved (relative to what could have been achieved for each goal alone with the same available resources). We suggest that, if the pCODR and the provincial drug plan decision-makers it supports want to strive for both goals simultaneously, they must prioritize the goals and explicitly identify the tradeoffs associated with the prioritization. This will ensure that the consequences of striving to simultaneously achieve both goals are explicit, transparent, and predictable for provincial drug plan decision-makers, physicians, patients, caregivers, and society as a whole.
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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 | 0.059 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".