Collaborative implementation science: a Can-SOLVE CKD case example
Bibliographic record
Abstract
ABSTRACT: Research is critical for uncovering new and effective therapies for better health outcomes, yet there remains a significant lag between identifying evidence-based interventions and implementing them into practice. Research teams can often be experienced in evidence generation, but less so in evidence implementation, underscoring the need for more customized tools to support them in this latter step. The implementation stage can be especially challenging given how strategies must be tailored to the unique end users and contexts of a given intervention. Therefore, our patient-oriented kidney research network sought to create an "Implementation Toolkit" and "Pathway to Implementation" guide to help research teams and their operational and clinical partners in implementing their interventions. Importantly, the tools were created using input and feedback from diverse groups, including patient partners, implementation science experts, researchers, operational leaders, and policymakers, all of whom play role in supporting the implementation of health interventions. Our tools are widely applicable to diverse teams, regardless of the intervention or innovation being implemented. SPANISH ABSTRACT: http://links.lww.com/IJEBH/A214.
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.
How this classification was reachedexpand
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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; both teacher heads 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".