Knowledge Translation in Audiology
Bibliographic record
Abstract
The impetus for evidence-based practice (EBP) has grown out of widespread concern with the quality, effectiveness (including cost-effectiveness), and efficiency of medical care received by the public. Although initially focused on medicine, EBP principles have been adopted by many of the health care professions and are often represented in practice through the development and use of clinical practice guidelines (CPGs). Audiology has been working on incorporating EBP principles into its mandate for professional practice since the mid-1990s. Despite widespread efforts to implement EBP and guidelines into audiology practice, gaps still exist between the best evidence based on research and what is being done in clinical practice. A collaborative dynamic and iterative integrated knowledge translation (KT) framework rather than a researcher-driven hierarchical approach to EBP and the development of CPGs has been shown to reduce the knowledge-to-clinical action gaps. This article provides a brief overview of EBP and CPGs, including a discussion of the barriers to implementing CPGs into clinical practice. It then offers a discussion of how an integrated KT process combined with a community of practice (CoP) might facilitate the development and dissemination of evidence for clinical audiology practice. Finally, a project that uses the knowledge-to-action (KTA) framework for the development of outcome measures in pediatric audiology is introduced.
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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 | 0.022 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".