On Being a Good Listener: Setting Priorities for Applied Health Services Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the last decade, explicit priority setting has become an integral part of health care systems. Indeed, there is even an International Society on Priorities in Health Care, created in 1997 ( Ham 1997 ). Whether it is Oregon's priority ordering of symptom treatment pairs to maximize the impact of a limited Medicaid budget (Fox and Leichter 1991), England's National Institute for Clinical Excellence's assessing priorities for new therapeutic innovations in the National Health Service ( Rawlins 1999 ), or New Zealand's setting priorities for patients' access to cardiovascular treatment ( Hadorn and Holmes 1997 ), techniques for judging the relative worth of different health service investments abound. As these techniques are refined, the most common addition is the incorporation of public values as part of the assessment. Priority setting is increasingly seen as combining an objective assessment of costs and effects with a more subjective assessment of patient or public preferences ( Lenaghan, New, and Mitchell 1996 ; Lomas 1997 ; National Institute for Clinical Excellence 2002 ; Stronks et al. 1997 ).
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.
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 | 0.040 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it