Introducing the 'Third Phase' of Priority Setting: Advancing Methods for Priority Setting Practice through the Contribution of Systems Theory. Lessons from a Case Study of District Health Planning and Priority Setting in Ethiopia
Bibliographic record
Abstract
Over the last 20 years, there has been considerable scholarly attention paid to developing priority setting methods with an emphasis on improving the evidence base of priority setting decisions, the use of explicit decision criteria, and the fairness of decision-making processes. Case studies of priority setting in health institutions and systems internationally have identified a variety of factors in practice that influence the effectiveness of these methods. There is a paucity of research, however, that empirically examines how these factors operate and to what extent they comprise enablers or barriers to fair priority setting. The purpose of this dissertation is to advance priority-setting methods by examining how systems theory may inform our understanding of priority setting through a case study of district health planning in Ethiopia. To fulfill this purpose, three objectives were undertaken. The first objective sought to describe the district health planning and priority setting process in Ethiopia. A qualitative case study of Ethiopian district health planning was undertaken in 2010 and 2011. Methods included 57 in-depth key informant interviews with decision makers, participant observation, and document analysis. The second objective sought to analyze this description through the theoretical lens of Accountability for Reasonableness (A4R) and the Transformative Systems Change Framework (TSCF). The third objective sought to conceptually synthesize these findings by situating priority setting practice and procedural fairness within a robust understanding of the system. The study findings reaffirm priority setting is a highly complex process that is value laden and influenced by a multiplicity of system-level factors. Through the application of the TSCF, a nuanced understanding of priority setting practice is understood that situates this process within a system of influencing components that include: norms, operations, regulations, and resources. Analysis reveals a number of system barriers and facilitators that impact not only the implementation of district health planning, but also the degree to which elements of procedural fairness are upheld. In light of these findings, I propose the introduction of a third phase in the priority setting discourse that emphasizes the need for methods and approaches inclusive of system-level considerations. I conclude with the development of a series of practical questions to guide practitioners in the design and implementation of their priority setting methods.
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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 | 0.110 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".