Decision maker views on priority setting in the Vancouver Island Health Authority
Bibliographic record
Abstract
BACKGROUND: Decisions regarding the allocation of available resources are a source of growing dissatisfaction for healthcare decision-makers. This dissatisfaction has led to increased interest in research on evidence-based resource allocation processes. An emerging area of interest has been the empirical analysis of the characteristics of existing and desired priority setting processes from the perspective of decision-makers. METHODS: We conducted in-depth, face-to-face interviews with 18 senior managers and medical directors with the Vancouver Island Health Authority, an integrated health care provider in British Columbia responsible for a population of approximately 730,000. Interviews were transcribed and content-analyzed, and major themes and sub-themes were identified and reported. RESULTS: Respondents identified nine key features of a desirable priority setting process: inclusion of baseline assessment, use of best evidence, clarity, consistency, clear and measurable criteria, dissemination of information, fair representation, alignment with the strategic direction and evaluation of results. Existing priority setting processes were found to be lacking on most of these desired features. In addition, respondents identified and explicated several factors that influence resource allocation, including political considerations and organizational culture and capacity. CONCLUSION: This study makes a contribution to a growing body of knowledge which provides the type of contextual evidence that is required if priority setting processes are to be used successfully by health care decision-makers.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.011 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".