Rapid transition to telepractice across the lifespan in speech-language pathology: Insight from a survey of clinicians in Canada
Bibliographic record
Abstract
This study aims to describe the experiences and needs of Canadian speech-language pathologists (SLPs) who conducted communication assessments via telepractice across the lifespan during the first year (2020) of the COVID-19 pandemic. The present study consisted of an online survey that aimed to capture both quantitative aspects of telepractice-based communication assessment and the qualitative experience of shifting to telepractice. One hundred sixty-eight practicing SLPs across Canada participated in the survey, between September 2020 and January 2021. Quantitative results were analysed using descriptive statistics while open-ended responses were analysed using thematic analysis. SLPs identified challenges and opportunities relating to client and family engagement, access to and knowledge of technology, and the reliability of assessment tools. SLPs also identified a future need for online assessment materials and training, such as materials adapted for different communication needs (e.g. augmentative and alternative communication). The present study contributes to a growing understanding worldwide of potential benefits and challenges related to telepractice, fuelled by the necessary shift in practices in our field during the COVID-19 pandemic. The results provide direction for continuing to build a valid and inclusive approach to telepractice in the future.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".