Exploring Caseload Data of Vision Professionals and Their Implications
Bibliographic record
Abstract
Introduction: To determine the current and future needs for teachers of students of visual impairments (TVIs), orientation and mobility (O&M) specialists, and dually certified professionals, information about caseloads is needed. However, few current studies exist that provide this data. Methods: The purpose of the study was to analyze demographic and caseload data gathered from 834 professionals who took part in a larger study. Descriptive and inferential statistics compared caseload size averages based on employment status, role, service delivery model, and region. Results: Minimal caseload differences existed among the regions of the United States, based on mean. Average caseload sizes by role reflected past literature. Caseloads ranged between 0 and 107 students, once outliers were removed. Discussion: This study provides some new data that provide insight into current caseloads, but a more expansive study would further contribute to the understanding of service provision for students with visual impairments. Implications for Practitioners: The field of visual impairment should continue to work toward recruiting a more diverse workforce and continue advocacy efforts for reasonable workloads and equitable services for students with visual impairments.
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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 | 0.015 | 0.125 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".