The Impact of COVID-19 on Speech-Language Pathologists Engaged in Clinical Practices With Elevated Coronavirus Transmission Risk
Bibliographic record
Abstract
Purpose This study assessed and described potential clinical practice changes secondary to COVID-19 that emerged as an early response to the pandemic for speech-language pathologists (SLPs) engaged in voice, alaryngeal, and swallowing activities that may increase the risk of virus transmission. Method SLPs from the United States and Canada ( n = 665) who were engaged in clinical activities that might elevate the risk of COVID-19 exposure completed an online survey regarding their clinical practices. Topics assessed included potential clinical service modifications, COVID-19 testing and health, and potential financial impacts in the early time period of the pandemic. Results The percentage of SLPs completing the most endoscopic procedures prepandemic (≥ 10/week) was reduced from 39% of respondents to 3% due to the pandemic. Those who completed the most tracheoesophageal puncture voice prosthesis changes (≥ 5/week) reported a reduction in frequency from 24% to 6%. Twenty-five percent of SLPs reported that they were tested for COVID-19, and 6% reported a positive result. Descriptive statistics suggest that COVID-19 testing rates of SLPs, the percentage of SLPs experiencing a financial impact, and the percentage who were furloughed varied across SLP work setting. Conclusions These findings provide the first data characterizing the impact on COVID-19 on clinical practice for SLPs engaged in procedures such as endoscopy and laryngectomy care. The results indicate that, as frontline workers, SLPs were directly impacted in their practice patterns, personal health, safety, and financial security, and that these reported impacts occurred differently across SLP work settings.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".