Telepractice troubleshooting guide (McGill & Fiddler, 2021)
Bibliographic record
Abstract
Purpose: Telepractice has been used as an alternative service delivery model in speech-language pathology across various settings and the scope of practice. Despite its utility and increasing demands resulting from the COVID-19 global pandemic, some clinicians and clients continue to report apprehension to adopting telepractice service delivery model due to discomfort with technology. Among currently available telepractice platforms, “ZOOM” is one of the popular platforms among speech-language pathologists (SLPs) in the United States because of its usability and subscription cost. However, many challenges have been experienced by clinicians and clients when ZOOM is used. The purpose of this article was twofold. The first goal of this article was to address barriers and challenges to implement successful telepractice SLP services. The second goal of this article was to develop four step-by-step troubleshooting manuals (one for client and one for clinician in both English and Spanish) to enhance its utility for users.Conclusion: Preliminary qualitative data showed that the manuals developed by the authors were useful and functional for graduate SLP students. Supplemental Material S1. Clinician English ZOOM manual. Supplemental Material S2. Client English ZOOM manual. Supplemental Material S3. Spanish manual for clinicians. Supplemental Material S4. Spanish manual for clients. McGill, M., & Fiddler, K. (2021). A user's guide for understanding and addressing telepractice technology challenges via ZOOM. Perspectives of the ASHA Special Interest Groups. Advance online publication. https://doi.org/10.1044/2021_PERSP-20-00100
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.537 | 0.336 |
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".