Pilot and feasibility studies: extending the conceptual framework
Bibliographic record
Abstract
In 2016, we published a conceptual framework outlining the conclusions of our work in defining pilot and feasibility studies. Since then, the CONSORT extension to randomised pilot and feasibility trials has been published and there have been further developments in the pilot study landscape. In this paper, we revisit and extend our framework to incorporate the various feasibility pathways open to researchers, which include internal pilot studies. We consider, with examples, when different approaches to feasibility and pilot studies are more effective and efficient, taking into account the pragmatic decisions that may need to be made. The ethical issues involved in pilot studies are discussed. We end with a consideration of the funders' perspective in making difficult resource decisions to include feasibility work and the policy implications of these; throughout, we provide examples of the uncertainties and compromises that researchers have to navigate to make progress in the most efficient way.
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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 | 0.660 | 0.537 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.009 | 0.083 |
| Scholarly communication | 0.026 | 0.051 |
| Open science | 0.010 | 0.020 |
| Research integrity | 0.024 | 0.026 |
| Insufficient payload (model declined to judge) | 0.008 | 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".