‘The Anglo-Saxon disease’: a pilot study of the barriers to and facilitators of the use of randomised controlled trials of social programmes in an international context: Table 1
Bibliographic record
Abstract
BACKGROUND: There appears to be considerable variation between different national jurisdictions and between different sectors of public policy in the use of evidence and particularly the use of randomised controlled trials (RCTs) to evaluate non-healthcare sector programmes. METHODS: As part of a wider study attempting to identify RCTs of public policy sector programmes and the reasons for variation between countries and sectors in their use, we carried out a pilot study which interviewed 10 policy makers and researchers in six countries to elicit views on barriers to and facilitators of the use of RCTs for social programmes. RESULTS: While in common with earlier studies, those interviewed expressed a need for unambiguous findings, timely results and significant effect sizes, users could, in fact, be ambivalent about robust methods and robust answers about what works, does not work or makes no difference, particularly where investment or a policy announcement was planned. Different national and policy sector cultures varied in their use of and support for RCTs. CONCLUSIONS: In order to maximise the use of robust evaluations of public programmes across the world it would be useful to examine, systematically, cross-national and cross-sectoral variations in the use of different methods including RCTs and barriers to and facilitators of their use. Sound research methods, whatever their scientific value, are no guarantee that findings will be useful or used. 'Stories' have been shown to influence policy; those advocating the use of RCTs may need to provide convincing narratives to avoid repetition about their value.
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 | 0.266 | 0.419 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".