Social values and cancer funding priorities: Empirical evidence for cancer policy.
Bibliographic record
Abstract
e18352 Background: Achieving value in health care requires knowledge of public values and priorities. To better understand public values for resource allocation on cancer care, we conducted a population-based stated preference survey with a nested randomized controlled moral reasoning intervention. Our objective was to generate evidence to inform economic evaluation and policymaking on cancer care priority-setting and payment reform in developed health systems. Methods: We conducted a population-based stated preference survey of societal views on the prioritization of health resources between children and adults, administered to a nationally representative sample (n = 1,556) of Canadian adults. Allocative preferences were elicited across a range of hypothetical treatment scenarios and scored on a visual analogue scale. Participants were randomized to a moral reasoning intervention (n = 773) or a control group (n = 783). Those randomized to the intervention group were exposed to a moral reasoning exercise prior to each choice task. The exercise presented participants with a list of ethical principles relevant to health care resource allocation and tasked them to select the top principles guiding their choice. The main outcomes were the difference in mean preference scores by group, scenario, and participant demographics. Results: Multiple regression analyses demonstrated a consistent aggregate preference by participants to allocate scarce health system resources to children. Exposure to the moral reasoning exercise weakened but did not eliminate allocative preference for children, as compared to control (difference 0.72, SE 0.14, p < 0.0001). Younger respondent age (-0.71, SE 0.14, p < 0.0001) and parenthood (-0.40, SE 0.11, p < 0.0002) were associated with greater preference for children. The top three principles guiding participants’ allocative decisions were treat equally (54.3-63.9%), relieve suffering (39.6-66.1%), and rescue those at risk of dying (37-40.8%). Conclusions: Our results demonstrate a significant preference by participants to allocate health care resources to children, but one attenuated by exposure to a range of ethical principles to guide decision-making. It also evinced strong support for humanitarian principles to guide health care resource allocation. Definitions of value in health care based primarily on the magnitude of clinical benefit and cost-effectiveness may exclude moral considerations that the public values.
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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 | 0.032 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.001 |
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".