Developing a shared language: a proposed guide to frame early implementation science collaboration discussions
Bibliographic record
Abstract
Miscommunication between health care practitioners and implementation researchers can lead to a mismatch of expectations and understandings, resulting in wasted research and frustration. Conversely, combining the expertise and knowledge of those working in health care practice and implementation research can deliver context informed research questions and appropriate study designs. Achieving this ambition requires a shared language. We sought to develop a guide to identify a common language to constructively explore nascent implementation research concepts. We set up a working group, comprising of implementation researchers, health care practitioners and operational managers, to work through ideas generation, debate and a consensus process to generate and refine a discussion guide. The resultant guide steps health care practitioners and implementation researchers through a three-phase enquiry - Question 1: What is the implementation question? Question 2: What is the proposed implementation solution? And Question 3: How can the investigation of this idea be resourced? At each step, the health care practitioner and implementation researcher collaborate to include theory and practice and rigorously work through the question to build implementation on evidence and to promote diverse stakeholder engagement. The next steps for this study will be operationalising the discussion guide, as an interactive tool. Future evaluation, to test effectiveness, acceptability and feasibility will be designed with health care practitioners and implementation researchers.
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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 | 0.172 | 0.143 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.012 | 0.021 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.016 | 0.020 |
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".