Advancing the Speed and Science of Implementation Using Mixed-Methods Process Mapping – Best Practice Recommendations
Bibliographic record
Abstract
Mixed-methods process mapping is a visualisation tool that identifies the steps, resources and personnel required to deliver a clinical practice, and has been previously used in an ad hoc manner to develop effective implementation strategies and solutions. To realise the potential of mixed-methods process mapping as an implementation tool, we aimed to develop and formalize the methodological steps and provide guidance for contemporary best practice approaches to using this approach for optimising implementation practice and research. Synthesising theory, evidence and expertise, we have identified 10 best practice recommendations and provide the first systematic framework for integrating mixed-methods process mapping into three core phases of health systems implementation, specifically: (1) engaging interest holders (and maintaining engagement), (2) identifying when, where, why, and to whom change is needed (and potential consequences), and, (3) identifying barriers and enablers, and co-designing implementation strategies. For each phase, we provide: (a) a rationale for using mixed-methods process mapping, (b) best practice guidance for combining mixed-methods process mapping with implementation practice and research, and (c) case studies exemplifying best practice. This article provides intelligence on mixed-methods process mapping to improve the consistency and quality of its use among implementation researchers and practitioners. We present a rationale, guidance, and practical tools for conducting mixed-methods process mapping to enhance the quality of implementation research and practice which can be used and adapted internationally. In doing so, it builds capacity and provides an opportunity for researchers and healthcare professionals to better understand and embed evidence-based innovations into health systems, improving service and client outcomes. Further research is needed to establish potential uses of mixed-methods process mapping to support other core components of implementation practice (e.g., adaptation), and to formally test the impact of this approach independently versus as part of a combination of implementation strategies.
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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 | 0.370 | 0.559 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.028 | 0.039 |
| Open science | 0.013 | 0.022 |
| Research integrity | 0.017 | 0.029 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".