Phase 1 of collaborative action around the implementation of virtual hearing aid care: Development of a clinical practice guideline
Bibliographic record
Abstract
RATIONALE: There is a growing demand for comprehensive, evidence-based, and accessible clinical practice guidelines (CPGs) to address virtual service delivery. This demand was particularly evident within the field of hearing healthcare during the COVID-19 pandemic, when providers were faced with an immediate need to offer services at a distance. Considering the recent advancement in information and communication technologies, the slow uptake of virtual care, and the lack of knowledge tools to support clinical integration in hearing healthcare, a Knowledge-to-Action Framework was used to address the virtual care delivery research-to-practice gap. AIMS AND OBJECTIVES: This paper outlines the development of a CPG specific to provider-directed virtual hearing aid care. Clinical integration of the guideline took place during the COVID-19 pandemic and in alignment with an umbrella project aimed at implementing and evaluating virtual hearing aid care incorporating many different stakeholders. METHOD: Evidence from two systematic literature reviews guided the CPG development. Collaborative actions around knowledge creation resulted in the development of a draft CPG (v1.9) and the mobilisation of the guideline into participating clinical sites. RESULTS AND CONCLUSION: Literature review findings are discussed along with the co-creation process that included 13 team members, from various research and clinical backgrounds, who participated in the writing, revising, and finalising of the draft version of the guideline.
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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 | 0.199 | 0.238 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".