The Impact of COVID-19 on Speech-Language Pathologists Engaged in Clinical Practices With Elevated Coronavirus Transmission Risk: A 2021 Follow-Up Study
Bibliographic record
Abstract
Purpose: This study assessed changes in speech-language pathology practice 1 year following the onset of the COVID-19 pandemic. The specific focus was on speech-language pathologists (SLPs) engaged in evaluation and treatment activities that pose an increased risk of virus transmission, namely, aerosol-generating procedures. Method: SLPs from the United States and Canada ( n = 609) who were engaged in clinical activities with an elevated risk of COVID-19 exposure completed an online survey regarding their clinical practices. Topics assessed included continuation, modification, or cessation of clinical services, personal protective equipment (PPE) use, COVID-19 infections and vaccinations among SLPs, and financial impacts. For comparison, the results from the 2020 survey are included, and the percentage change (2020% − 2021%) is reported. Results: A majority of SLPs (90%) who completed the current survey reported that they were not experiencing PPE shortages, a marked change from the 2020 survey. Over half of the SLPs (52%) reported changes in clinical duties in 2021, with in-person visits and endoscopy being the most impacted. Nearly half of the respondents (49%) reported conducting virtual/telehealth clinical activities during the pandemic, and of these, 78% had not done any virtual/telehealth work prior to COVID-19. A positive COVID-19 test was reported by 10% of the SLPs (5% higher than in 2020). Most SLPs (92%) indicated they were vaccinated at the time of this survey. Conclusions: The 2021 survey data indicated an overall increase in SLP clinical activities compared with 2020 but have not yet returned to prepandemic levels. These results highlight a notable uptake of virtual/telehealth practice by SLPs, including those who had not previously done so. SLP vaccination rates also exceeded that of the general population.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".