Procedural fairness for radiotherapy priority setting in a low resource context
Bibliographic record
Abstract
Radiotherapy is an essential component of cancer treatment, yet many countries do not have adequate capacity to serve their populations. This mismatch between demand and supply creates the need for priority setting. There is no widely accepted system to guide patient prioritization for radiotherapy in a low resource context. In the absence of consensus on allocation principles, fair procedures for priority setting should be established. Research is needed to understand what elements of procedural fairness are important to decision makers in diverse settings, assess the feasibility of implementing fair procedures for priority setting in low resource contexts, and improve these processes. This study presents the views of decision makers engaged in everyday radiotherapy priority setting at a cancer center in Rwanda. Semi-structured interviews with 22 oncology physicians, nurses, program leaders, and advisors were conducted. Participants evaluated actual radiotherapy priority setting procedures at the program (meso) and patient (micro) levels, reporting facilitators, barriers, and recommendations. We discuss our findings in relation to the leading Accountability for Reasonableness (AFR) framework. Participants emphasized procedural elements that facilitate adherence to normative principles, such as objective criteria that maximize lives saved. They ascribed fairness to AFR's substantive requirement of relevance more than transparency, appeals, and enforcement. They identified several challenges unresolved by AFR, such as conflicting relevant rationales and unintended consequences of publicity and appeals. Implementing fair procedure itself is resource intensive, a paradox that calls for innovative, context-appropriate solutions. Finally, socioeconomic and structural barriers to care that undermine procedural fairness must be addressed.
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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 | 0.104 | 0.125 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.046 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".