Past, present and future challenges in health care priority setting
Bibliographic record
Abstract
Purpose Current conditions have intensified the need for health systems to engage in the difficult task of priority setting. As the search for a "magic bullet" is replaced by an appreciation for the interplay between evidence, interests, culture, and outcomes, progress in relation to these dimensions requires assessment of achievements to date and identification of areas where knowledge and practice require attention most urgently. The paper aims to discuss these issues. Design/methodology/approach An international survey was administered to experts in the area of priority setting. The survey consisted of open-ended questions focusing on notable achievements, policy and practice challenges, and areas for future research in the discipline of priority setting. It was administered online between February and March of 2015. Findings "Decision-making frameworks" and "Engagement" were the two most frequently mentioned notable achievements. "Priority setting in practice" and "Awareness and education" were the two most frequently mentioned policy and practical challenges. "Priority setting in practice" and "Engagement" were the two most frequently mentioned areas in need of future research. Research limitations/implications Sampling bias toward more developed countries. Future study could use findings to create a more concise version to distribute more broadly. Practical implications Globally, these findings could be used as a platform for discussion and decision making related to policy, practice, and research in this area. Originality/value Whilst this study reaffirmed the continued importance of many longstanding themes in the priority setting literature, it is possible to also discern clear shifts in emphasis as the discipline progresses in response to new challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.136 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".