Challenges and Opportunities for Speech‐Language Pathology Services in Comprehensive Head and Neck Cancer Care: Insights From a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Speech-language pathologists serve a critical role within multidisciplinary head and neck cancer care teams. Provision of speech-language pathology services for head and neck cancer patients varies by region and is not well characterized. METHODS: A mixed methods scoping assessment was conducted with a purposive sample of speech-language pathologists from designated comprehensive head and neck cancer centers. Each speech-language pathologist completed a 31-item survey and 60-min semi-structured interview. RESULTS: Analysis of survey responses and qualitative interviews identified three major themes: unsuitable infrastructure; multilevel barriers; and the need to champion speech-language pathology services. Speech-language pathologists consistently reported inadequate resources, inequitable services, and increasing job responsibilities associated with growing patient complexity and caseloads. CONCLUSIONS: Significant systemic barriers impede the effective delivery of speech-language pathology services in head and neck cancer care. Our findings and recommendations create an important foundation for healthcare agency decisions on the allocation and funding of speech-language pathology services.
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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 | 0.073 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".