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
BACKGROUND: In this article, we analyze one case instance of how proposals for change to the priority setting and resource allocation (PSRA) processes at a Canadian healthcare institution reached the decision agenda of the organization's senior leadership. We adopt key concepts from an established policy studies framework - Kingdon's multiple streams theory - to inform our analysis. METHODS: Twenty-six individual interviews were conducted at the IWK Health Centre in Halifax, NS, Canada. Participants were asked to reflect upon the reasons leading up to the implementation of a formal priority setting process - Program Budgeting and Marginal Analysis (PBMA) - in the 2012/2013 fiscal year. Responses were analyzed qualitatively using Kingdon's model as a template. RESULTS: The introduction of PBMA can be understood as the opening of a policy window. A problem stream - defined as lack of broad engagement and information sharing across service lines in past practice - converged with a known policy solution, PBMA, which addressed the identified problems and was perceived as easy to use and with an evidence-base from past applications across Canada and elsewhere. Conditions in the political realm allowed for this intervention to proceed, but also constrained its potential outcomes. CONCLUSION: Understanding in a theoretically-informed way how change occurs in healthcare management practices can provide useful lessons to researchers and decision-makers whose aim is to help health systems achieve the most effective use of available financial resources.
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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 | 0.030 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.025 | 0.016 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".