Evidence-based priority-setting: what do the decision-makers think?
Bibliographic record
Abstract
OBJECTIVES: Resource scarcity dictates the need for health organisations to set priorities. Although such activity should be based, at least in part, on evidence, there are limited examples in the literature of decision-makers reflecting on their use of evidence in priority-setting. METHODS: A participatory action-research project was conducted in a single health authority in Alberta. It included in-depth interviews and focus groups with senior decision-makers both before and after development and implementation of a macro-level priority-setting framework (programme budgeting and marginal analysis, PBMA). Data were thematically coded and information on the use of evidence in priority-setting is reported. RESULTS: Barriers to the use of evidence in priority-setting identified by decision-makers included crisis-orientated management, time constraints and a lack of skills. Decision-makers suggested using a mix of 'soft' and 'hard' forms of evidence in priority-setting. Following PBMA implementation, decision-makers wanted better information on capacity to benefit, but preferred to do this pragmatically from multiple sources of information rather than using a single metric. CONCLUSION: In examining the perspectives of decision-makers in using evidence to support priority-setting, valuable information was derived which should provide insight for such processes in other jurisdictions. The main finding of a desire for pragmatic assessment of benefit is informative for those involved in both decision-making and research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.130 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".