Determining social values for resource allocation decision-making in cancer care: a Canadian experiment
Bibliographic record
Abstract
To determine the content values that 2 separate juries of individuals consider to be important in making decisions about resource allocation in cancer care. Two citizens’ juries were established through random and stratified sampling of the population of Northern and Southern Alberta respectively. Four deliberative sessions were run identically in both juries. Juries participated in exercises, in small groups as well as in plenary. In an exercise in which they had to select 5 out of 10 cancer technologies for funding, the juries separately identified the factors they considered to be important for resource allocation decision-making. Socioeconomic measures between the 2 juries of 16 individuals did not differ significantly. The juries independently arrived at an identical list of content values that they deemed important to them to have included in decision-making processes. These were: number of patients who could benefit, current health state, prognosis without the technology, health outcome with the technology, age, and dependents. They also identified “levels” of these values, 2 for number of patients (many, few), 3 for current health state (severely, mildly and moderately ill), 3 for prognosis without technology (a few weeks, 2 years and 5 years for survival), 3 for health outcome with the technology (full functioning, sufficient functioning, insufficient functioning), 2 for age (old, young) and 2 for dependents (yes, no). Given appropriate design and delivery, Citizens’ Juries can deliberate on complex health issues and reach similar conclusions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".