Building bridges: The integral presence of audiologists and speech-language pathologists in comprehensive interprofessional primary care teams
Bibliographic record
Abstract
AIM: This article provides an overview of the professional roles of audiologists and speech-language pathologists (S-LPs) in interprofessional primary care. BACKGROUND: Current published literature considering primary care delivery within comprehensive interprofessional teams contains little representation of professionals from the fields of audiology and speech-language pathology. METHODS: An illustrative case scenario highlights the key roles of audiologists and S-LPs in primary care, and how collaborative relationships within an interprofessional primary care team structure can enhance the overall quality of care provided to patients and to their families. FINDINGS: As experts in the prevention, diagnosis, and rehabilitation of communication disorders, with S-LPs supporting speech and swallowing disorders and audiologists supporting hearing and vestibular disorders, S-LPs and audiologists are well-positioned to support meaningful participation in primary care across the lifespan and in collaboration with different healthcare professionals, including patients experiencing cognitive decline.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".