Well-functioning primary care systems through audiology and speech-language pathology integration: a scoping review
Bibliographic record
Abstract
PURPOSE: This scoping review sought to describe how primary care teams including audiologists and speech-language pathologists (S-LPs), embody well-functioning system properties, within the Theory of Systems Change. MATERIALS AND METHODS: This review was completed in accordance with the Joanna Briggs Institute Manual for Evidence Synthesis. MEDLINE, Cochrane Central Register of Controlled Trials, Embase and Embase Classic, APA PsycInfo, CINAHL, Scopus, and Web of Science databases were searched for articles focusing on (1) audiologists and S-LPs and (2) primary care teams. Retrieved articles then underwent a screening and extraction process. RESULTS: Forty-six studies were identified. Considering the components of the Theory of Systems Change, all studies demonstrated evidence-driven action and learning. Forty-two studies discussed adaptation strategies to external challenges. Teams aligned their work with micro, meso, macro, and mega system level considerations. Collaboration occurred through team meetings, information technology, care coordination, role clarification and negotiation, and joint care delivery. CONCLUSIONS: Bridging systems level and clinical theories can provide better context to advocate for the integration of these two professions into primary care teams. Audiologists and S-LPs enable patient/family participation which allows healthcare providers to gather the necessary information through communication focused care that can effectively integrate clinical and systems level frameworks.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".