Combining evidence and values in priority setting: testing the balance sheet method in a low-income country
Bibliographic record
Abstract
BACKGROUND: Procedures for priority setting need to incorporate both scientific evidence and public values. The aim of this study was to test out a model for priority setting which incorporates both scientific evidence and public values, and to explore use of evidence by a selection of stakeholders and to study reasons for the relative ranking of health care interventions in a setting of extreme resource scarcity. METHODS: Systematic search for and assessment of relevant evidence for priority setting in a low-income country. Development of a balance sheet according to Eddy's explicit method. Eight group interviews (n-85), using a modified nominal group technique for eliciting individual and group rankings of a given set of health interventions. RESULTS: The study procedure made it possible to compare the groups' ranking before and after all the evidence was provided to participants. A rank deviation is significant if the rank order of the same intervention differed by two or more points on the ordinal scale. A comparison between the initial rank and the final rank (before deliberation) showed a rank deviation of 67%. The difference between the initial rank and the final rank after discussion and voting gave a rank deviation of 78%. CONCLUSION: Evidence-based and deliberative decision-making does change priorities significantly in an experimental setting. Our use of the balance sheet method was meant as a demonstration project, but could if properly developed be feasible for health planners, experts and health workers, although more work is needed before it can be used for laypersons.
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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 | 0.411 | 0.518 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".