What Is Implementation Research?
Bibliographic record
Abstract
Despite the growing knowledge base on evidence-based practices in social work and medicine, there is a large gap between what is known and what is consistently done. Implementation research is the study of methods to promote the uptake of research findings into routine practice. In this article, we describe the rationale for implementation research and outline the concepts and effectiveness of its practices. Despite a large number of systematic reviews of implementation interventions, many of the fundamental questions regarding what approaches should be used in which settings for which problems remain unanswered. We go on to argue that future implementation studies should assess the context of practice and key features of interventions to better inform service quality improvement efforts.
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 | 0.576 | 0.734 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.008 | 0.043 |
| Scholarly communication | 0.043 | 0.061 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.027 | 0.029 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".