An Online Learning Module on the Role of Audiologists in Team-Based Primary Care: A Quality Improvement Assessment
Bibliographic record
Abstract
INTRODUCTION: The rapidly changing hearing health landscape, paired with health system resource and delivery challenges, presents the need to develop innovative learning opportunities that prepare health care professionals to work as part of an interprofessional team. METHODS: An interprofessional team of clinicians and researchers developed an online learning module titled The Role of Audiologists in Team-Based Primary Care . To support quality improvement efforts, learners were asked to complete pre- and post-module surveys, which included multiple-choice knowledge questions and Likert-scale statements assessing self-efficacy, value, and knowledge of learners' premodule and postmodule completion. RESULTS: Forty-seven learners participated in a premodule and postmodule assessment. Pre-module to post-module responses increased for all three domains evaluated: knowledge, self-efficacy, and value. The findings were not significant for the value of audiology in team-based primary care, indicating the need for greater efforts to encourage buy-in among learners through experiential learning opportunities and integration of content into mandated training. DISCUSSION: Preliminary findings from this quality assessment highlight that a novel online learning module focused on the roles of audiologists in team-based primary care can affect learners' knowledge, self-efficacy, and values regarding the topic. Future evaluations assessing the impact of module implementation alongside in-person activities can further improve learners' outcomes.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".