Implementation Science: Buzzword or Game Changer?
Bibliographic record
Abstract
PURPOSE: The purpose of this supplement article is to provide a resource of pertinent information concerning implementation science for immediate research application in communication sciences and disorders. METHOD: Key terminology related to implementation science is reviewed. Practical suggestions for the application of implementation science theories and methodologies are provided, including an overview of hybrid research designs that simultaneously investigate clinical effectiveness and implementation as well as an introduction to approaches for engaging stakeholders in the research process. A detailed example from education is shared to show how implementation science was utilized to move an intervention program for autism into routine practice in the public school system. In particular, the example highlights the value of strong partnership among researchers, policy makers, and frontline practitioners in implementing and sustaining new evidence-based practices. CONCLUSIONS: Implementation science is not just a buzzword. This is a new field of study that can make a substantive contribution in communication sciences and disorders by informing research agendas, reducing health and education disparities, improving accountability and quality control, increasing clinician satisfaction and competence, and improving client outcomes.
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 | 0.059 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".