RAPIT- A tool for Improving and Evaluating Priority Setting and Resource Allocation Internationally : A Swedish Health Authority Case Study
Bibliographic record
Abstract
IntroductionIn order to meet the challenges presented by increasing demand and scarcity of resources, healthcare organizations are faced with difficult decisions related to priority setting and resource allocation (PSRA). Tools to facilitate evaluation and improvement of these processes could enable greater transparency and more optimal distribution of resources. Resource Allocation Performance Identification Tool (RAPIT) is a tool for identifying the performance of PSRA. RAPIT was first developed in Canada (RAPAT - Resours Allocation Performance Assessment Tool) and then further developed in Sweden where items from the original tool were edited, likert scales were added, aiming to also identify areas of improvement. RAPIT was applied in the Regional Health Authority of Dalarna – ”Landstinget Dalarna”, Sweden, and administered to both middle and senior managers in the regional health authority Dalarna at baseline. In addition to answering the questions themselves, respondents were also asked to rate the value of each question with respect to PSRA performance relevance. Results were then presented to the senior managers. In this step, the senior mangers were asked to assess the value of information that they received from both the middle and senior managers responses. The senior managers also assessed the value of using RAPIT itself as an input for improving their PSRA process.ResultRAPIT revealed variations in the understanding of PSRA across management levels and individuals. 94% of the questions was given a very high value by the middle manager, 65% by senior manager. Some of the low ranked questions generated high valued information. In several cases, after seeing the result of RAPIT, senior managers changed their assessed value of ech question. Concerning incentives to participation for example, the senior mangers valued the question low but after seeing the result they valued the information very high.ConclusionsRAPIT is useful for identifying areas of improvement in a PSRA process: • Useful for education and promoting discussion – what does our PSRA process really look like? • Useful for motivating a more explicit PSRA process • Useful for mutual discussion when PSRA is perceived differently by involved actor. • Useful for a longitudinal perspective on the improvement on targeted areas • Useful as a collection of questions that can be adjusted to a specific context • Useful as an evaluation tool • Useful for comparative studies, not least for international comparisons. RAPIT is applicable in multiple contexts, and enables decision makers at different level to identify opportunities to improve their own PSRA processes.
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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 | 0.033 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".