Better evidence about wicked issues in tackling health inequities
Bibliographic record
Abstract
The need for better evidence continues to be emphasized by both researchers and users of research. ‘Better evidence for a better world’ was the theme of this year's Campbell Colloquium for example, a gathering of researchers and policy-makers who aim to systematically build the evidence on the effectiveness of social policies and programs, including those aimed at improving social welfare, reducing crime and improving educational outcomes. This resonates with continuing political interest in making policies more evidence informed. In keeping with this spirit of evidence-based enquiry, it is reasonable to consider what ‘better evidence’ might look like. One important way to improve current evidence on health and social outcomes is to consider, and report on, the impact of interventions on health equity. Currently much research on the effects of interventions, which address the social determinants of health is focused on the mean population-level effects and less on the differential effects within populations, such as on differences in effectiveness for those who are disadvantaged. This ‘evidence with a utilitarian bias’1 results in insufficient evidence on how to adequately address health inequalities. Primary studies often fail to collect, analyse and present data on differential effects, and even where such data are available, systematic reviews often fail to use it.2,3 ‘Better evidence’, then, means evidence that explores the effects of interventions within different sections of society, and that can be used to tackle inequalities in health. In the absence of this information we may proceed on best guesses and practical experience; however, in both clinical and population-level interventions, we know that current practice and conventional wisdom are often poor guides to what works.4
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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 | 0.247 | 0.577 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.013 | 0.033 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.037 | 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 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".