Priority Setting Meets Multiple Streams: A Match to Be Further Examined? Comment on "Introducing New Priority Setting and Resource Allocation Processes in a Canadian Healthcare Organization: A Case Study Analysis Informed by Multiple Streams Theory
Bibliographic record
Abstract
With demand for health services continuing to grow as populations age and new technologies emerge to meet health needs, healthcare policy-makers are under constant pressure to set priorities, ie, to make choices about the health services that can and cannot be funded within available resources. In a recent paper, Smith et al apply an influential policy studies framework - Kingdon's multiple streams approach (MSA) - to explore the factors that explain why one health service delivery organization adopted a formal priority setting framework (in the form of programme budgeting and marginal analysis [PBMA]) to assist it in making priority setting decisions. MSA is a theory of agenda-setting, ie, how it is that different issues do or do not reach a decision-making point. In this paper, I reflect on the use of the MSA framework to explore priority setting processes and how the framework might be applied to similar cases in future.
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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 | 0.020 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.062 | 0.063 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".