Guidance for conducting feasibility and pilot studies for implementation trials
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
BACKGROUND: Implementation trials aim to test the effects of implementation strategies on the adoption, integration or uptake of an evidence-based intervention within organisations or settings. Feasibility and pilot studies can assist with building and testing effective implementation strategies by helping to address uncertainties around design and methods, assessing potential implementation strategy effects and identifying potential causal mechanisms. This paper aims to provide broad guidance for the conduct of feasibility and pilot studies for implementation trials. METHODS: We convened a group with a mutual interest in the use of feasibility and pilot trials in implementation science including implementation and behavioural science experts and public health researchers. We conducted a literature review to identify existing recommendations for feasibility and pilot studies, as well as publications describing formative processes for implementation trials. In the absence of previous explicit guidance for the conduct of feasibility or pilot implementation trials specifically, we used the effectiveness-implementation hybrid trial design typology proposed by Curran and colleagues as a framework for conceptualising the application of feasibility and pilot testing of implementation interventions. We discuss and offer guidance regarding the aims, methods, design, measures, progression criteria and reporting for implementation feasibility and pilot studies. CONCLUSIONS: This paper provides a resource for those undertaking preliminary work to enrich and inform larger scale implementation trials.
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 | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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