Evidence and the health policy process: from traditional evidence hierarchy to inclusive and multi-source methodology
Bibliographic record
Abstract
Calls for evidence-based health policy have gathered force as an extension of the movement for evidence-based medicine. In clinical medicine, major investment has been made in efforts to systematize the collection and analysis of data and distinguish effective interventions from those that are less likely to work. In contrast, there is little consensus on what data are needed and what research methods are suitable and acceptable to produce a robust evidence base for social policy in the health sector. Evidence gathering for health policy must synthesise diverse sources, recognise the extent to which context influences policy outcomes, accommodate potentially conflicting interests and be flexible enough to respond to the time and resources pressures that are at play. Despite the challenges, there is scope for the development of a methodology that can draw on a wide range of evidence sources while retaining sufficient scientific rigour. These sources should extend from data generated using causal methods (randomized controlled trials) to information that can shed light on the many contextual and political issues that are also pertinent to health policy decision making.
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 | 0.492 | 0.560 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.004 |
| Bibliometrics | 0.053 | 0.029 |
| Science and technology studies | 0.008 | 0.066 |
| Scholarly communication | 0.042 | 0.045 |
| Open science | 0.012 | 0.031 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier 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".