Connecting the science and practice of implementation – applying the lens of context to inform study design in implementation research
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
The saying "horses for courses" refers to the idea that different people and things possess different skills or qualities that are appropriate in different situations. In this paper, we apply the analogy of "horses for courses" to stimulate a debate about how and why we need to get better at selecting appropriate implementation research methods that take account of the context in which implementation occurs. To ensure that implementation research achieves its intended purpose of enhancing the uptake of research-informed evidence in policy and practice, we start from a position that implementation research should be explicitly connected to implementation practice. Building on our collective experience as implementation researchers, implementation practitioners (users of implementation research), implementation facilitators and implementation educators and subsequent deliberations with an international, inter-disciplinary group involved in practising and studying implementation, we present a discussion paper with practical suggestions that aim to inform more practice-relevant implementation research.
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 | 0.105 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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