Critical actions for embedding research evidence into practice: how to get the most out of your implementation scientist
Bibliographic record
Abstract
ABSTRACT: Implementation science has been gaining traction over the last decade to support health care systems in adopting and sustaining evidence-based interventions, programs, and policies. Given the inherent complexity of implementation research and practice, and their associated methodologies, implementation scientists play a central role in translating research into practice. However, many health care system stakeholders often struggle to understand how best to collaborate with implementation scientists. This commentary discusses the significant benefits of such collaboration, outlining ten critical actions drawn from the collective experience of 25 implementation scientists with over 173 years of combined expertise. This project was conducted under the SPHERE Implementation Science Platform, as part of the Sydney Partnership for Health, Education, Research and Enterprise (SPHERE).The ten recommendations for working with an implementation scientist to optimize implementation efforts include the following: (1) involve implementation scientists early during intervention design, (2) recognize the unique nature and value of implementation science data, (3) integrate implementation assessments into the research plan, (4) foster collaborative partnerships inclusive of implementation science, (5) differentiate between factors affecting implementation and wider constraints, (6) work with implementation scientists to address implementation challenges, (7) prioritize implementation scale and sustainment, (8) embrace that implementation requires continuous learning and adaptation, (9) promote knowledge exchange between implementation science and subject matter experts, and (10) focus on capability- and capacity-building for implementation within the system. By following these recommendations, researchers, clinicians, decision-makers, and implementation scientists can foster impactful collaborations that enhance the translation of research into clinical practice and improve the quality of health care delivery. SPANISH ABSTRACT: http://links.lww.com/IJEBH/A374.
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 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 | 0.549 | 0.712 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.026 | 0.107 |
| Scholarly communication | 0.063 | 0.076 |
| Open science | 0.016 | 0.037 |
| Research integrity | 0.063 | 0.137 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".