Phase 2 of collaborative action around the implementation of virtual hearing aid care: Evaluation of a clinical practice guideline
Bibliographic record
Abstract
RATIONALE: Following the onset of the COVID-19 pandemic, a clinical practice guideline (CPG) around virtual hearing aid practices was developed to fill a knowledge gap within the field of audiology. Details outlining the development and mobilization of this draft guideline were outlined as Phase 1 (described in a paired paper). AIMS AND OBJECTIVES: This study describes Phase 2 of this project as part of the Knowledge-to-Action Framework, including an evaluation of the methodological quality of the guideline and the resulting tailored version of the document (v2.0). METHOD: The Appraisal of Guidelines for Research and Evaluation II instrument was used to assess methodological quality and to guide revisions. Twenty-two clinicians, from a variety of clinical backgrounds, participated in the evaluation. RESULTS AND CONCLUSION: Findings reported across six domains suggest high mean scores, ranging from 78% to 81%, in order of scope and purpose (highest rated), stakeholder involvement, rigour of development, applicability, clarity of presentation, and editorial independence. Specific recommendations made by in international co-creation team during the evaluation informed the final version of the CPG. Future development and evaluation efforts should aim to include greater representation from nontraditional practice contexts to strengthen global applicability.
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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 | 0.232 | 0.343 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".