Addressing Bias in SLP Problem-Based Tutorials through Critical Reflexivity, Curriculum Development and Instructor Training
Bibliographic record
Abstract
Racism is prevalent in the fields of healthcare and education in North America and speech-language pathology and audiology are no exception. Systemic and individual racism in educational, training, and clinical settings creates barriers for student entry and success, and negatively impacts client care. Although the ability to serve clients of diverse backgrounds is a crucial skill for students and clinicians, current educational curricula appears insufficient in supporting culturally diverse students and preparing all students to work with culturally diverse populations. This is, in part, due to a lack of diverse representation in education and clinical settings, bias experienced by SLP and audiology students in education programs, and problematic ways in which clinical information and race are presented in these educational programs. This paper aims to provide evidence informed guidance to SLP and audiology educators that will support their efforts to: 1. Develop students’ critical reflection and critical reflexivity skills. 2. Integrate racial and cultural diversity in the curricula. 3. Develop instructor competencies to create a safe learning environment. An example of a problem-based tutorial course in an SLP program is presented with a focus on clinical case development and small group learning experiences. Revision of curricula content with a focus on developing students’ lifelong skills in critical reflexivity may provide a foundation to equip SLPs and audiologists to address existing health disparities and improve client outcomes.
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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 | 0.042 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".