What do District Health Planners in Tanzania think about improving priority setting using 'Accountability for Reasonableness'?
Bibliographic record
Abstract
BACKGROUND: Priority setting in every health system is complex and difficult. In less wealthy countries the dominant approach to priority setting has been Burden of Disease (BOD) and cost-effectiveness analysis (CEA), which is helpful, but insufficient because it focuses on a narrow range of values - need and efficiency - and not the full range of relevant values, including legitimacy and fairness. 'Accountability for reasonableness' is a conceptual framework for legitimate and fair priority setting and is empirically based and ethically justified. It connects priority setting to broader, more fundamental, democratic deliberative processes that have an impact on social justice and equity. Can 'accountability for reasonableness' be helpful for improving priority setting in less wealthy countries? METHODS: In 2005, Tanzanian scholars from the Primary Health Care Institute (PHCI) conducted 6 capacity building workshops with senior health staff, district planners and managers, and representatives of the Tanzanian Ministry of Health to discussion improving priority setting in Tanzania using 'accountability for reasonableness'. The purpose of this paper is to describe this initiative and the participants' views about the approach. RESULTS: The approach to improving priority setting using 'accountability for reasonableness' was viewed by district decision makers with enthusiastic favour because it was the first framework that directly addressed their priority setting concerns. High level Ministry of Health participants were also very supportive of the approach. CONCLUSION: Both Tanzanian district and governmental health planners viewed the 'accountability for reasonableness' approach with enthusiastic favour because it was the first framework that directly addressed their concerns.
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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 | 0.051 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".