Confidence and Training of Speech-Language Pathologists in Cognitive-Communication Disorders: Time to Rethink Graduate Education Models?
Bibliographic record
Abstract
Purpose The purpose of this article is to highlight the need for increased focus on cognitive communication in North American speech-language pathology graduate education models. Method We describe key findings from a recent survey of acute care speech-language pathologists (SLPs) in the United States and expand upon the ensuing discussion at the 2020 International Cognitive-Communication Disorders Conference to consider some of the specific challenges of training for cognitive communication and make suggestions for rethinking how to prepare future clinicians to manage cognitive-communication disorders. Results Results from the survey of acute care SLPs indicated inconsistent confidence and training in managing cognitive-communication disorders. We discuss the pros and cons of several avenues for improving the consistency of cognitive-communication training, including a standalone cognitive-communication course, integrating cognitive communication in all courses across the speech-language pathology undergraduate and graduate curriculum, and using problem-based learning frameworks to better prepare students as independent thinkers in the area of cognitive communication and beyond. Conclusions Cognitive-communication disorders cut across clinical diagnoses and settings and are one of the largest and fastest growing parts of the SLP's scope of practice. Yet, surveys, including the one discussed here, have repeatedly indicated that SLPs do not feel prepared or confident to work with individuals with cognitive-communication disorders. We propose several avenues for increasing educational emphasis on cognitive communication. We hope these ideas will generate discussion and guide decision making to empower SLPs to think critically and step confidently into their roles as leaders in managing the heterogeneous and ever-growing populations of individuals with cognitive-communication disorders.
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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 | 0.049 | 0.142 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".