What Makes a Caseload (Un)Manageable? School-Based Speech-Language Pathologists Speak
Bibliographic record
Abstract
PURPOSE: Large caseload sizes and a shortage of speech-language pathologists (SLPs) are ongoing concerns in the field of speech and language. This study was conducted to identify current mean caseload size for school-based SLPs, a threshold at which caseload size begins to be perceived as unmanageable, and variables contributing to school-based SLPs' feelings of caseload manageability. METHOD: Approximately 2,000 public-school-based SLPs from across the country were solicited to participate in an online, Web-based survey between April and May of 2007. Of those SLPs who were contacted, 634 full-time SLPs from 49 states completed the survey. The data were evaluated using descriptive statistics and logistic regression. RESULTS: The mean caseload size for SLPs in this study was 49 students. At the caseload range of 41-50 students, approximately 60% of the SLPs perceived their caseload size as unmanageable. Logistic regression revealed caseload size, years of experience, and extent of collaboration as significant predictors of an SLP's likelihood of feeling that his or her caseload size is manageable. CONCLUSIONS: Caseload size continues to be an area of concern for school-based SLPs, and efforts to address this problem must continue in order to prevent long-term struggles with SLPs' dissatisfaction, shortages, and turnover. Policy, research, and clinical implications are discussed.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".