The Information Needs of School-based Speech–Language Pathologist Assistants: A Case Study
Bibliographic record
Abstract
Speech–language pathologists (SLPs) are professionals who specialize in diagnosing and treating communication disorders. They are sometimes supported by a speech–language pathologist assistant (SLPA), who engages in treatment procedures under the guidance of the supervising SLP. Both the qualifications needed to practice as well as the scope of responsibilities vary for SLPAs depending on jurisdiction. Notably, these assistants can play a central role in the treatment of speech disorders. Research regarding the information needs of SLPAs, however, is limited. This paper seeks to explore the information resources and services available to a particular SLPA community and to examine the obstacles to meeting its information needs. An interview with a practicing school-based SLPA is used to discuss current practices and suggest improvements to information services available to SLPA communities. This interview highlights some of the challenges that may be faced by school-based SLPAs when seeking information, and it provides an opportunity to consider context-specific solutions to these issues.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".