Leveraging intermediaries’ skillsets to build implementation research and practice infrastructure: a qualitative case study
Bibliographic record
Abstract
Abstract Background Implementation science as a field is rapidly advancing. Moreover, implementation science plays a pivotal role in driving learning health systems to better realize health outcomes and impact for our communities. Yet, few reports detail the infrastructures that underpin embedding and managing implementation science activities. Furthermore, there is little guidance for designing these infrastructures (people-powered and/or inanimate supports essential for embedding implementation research questions in pilot, spread and scale initiatives) to address local research and practice needs. The Implementation Science Collaborative is one such infrastructure in Alberta, Canada that leverages existing expertise in implementation research and practice to facilitate embedded implementation research and increase the success rates of health innovation implementation for better health outcomes. This study sought to provide actionable recommendations for designing effective implementation infrastructure by examining the co-design of the Implementation Science Collaborative. Methods We conducted a longitudinal case study (2018–2021) of the Implementation Science Collaborative using document analysis and semi-structured interviews. We collected data from initiative planning and operations documents (n = 190) and semi-structured interviews with Implementation Science Collaborative members (n = 6). We applied the Large-Scale Change Driver Model as the analytical framework for qualitative analysis to generate insights into designing cross-sectoral implementation science infrastructure. Results Our analysis showed that infrastructure design and operationalization followed established principles of implementation planning and execution. Implementation intermediaries proved to be effective facilitators as they had the backgrounds required to guide co-design and implementation planning. Their political neutrality in the resulting infrastructure enabled them to address power imbalances among co-design partners. However, strong management leadership remained irreplaceable. Cross-sectoral leadership was essential in fostering and solidifying the partnerships required for supporting the local learning health system. Conclusion The study findings highlight the effectiveness of a co-design approach, facilitated by intermediaries, in developing local implementation science infrastructure and management systems as a promising practice to implement for achieving outcomes. This approach enabled the creation of infrastructure designs that meet diverse user needs. However, co-design is a complex process that benefits from both intermediaries’ skills and cross-sectoral leadership knowledge of the local learning health system.
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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 | 0.066 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".