Who are the Speech-Language Pathologists of the Future? Results of a national demographic survey of Canadian SLP Students
Bibliographic record
Abstract
Abstract Speech-language pathologists (SLPs) are experts in communication and swallowing who work with individuals across the lifespan. Prior demographic surveys in English-speaking countries (e.g., Canada and the United States) suggest that SLPs’ gender, racial, linguistic, and cultural and socioeconomic backgrounds may not be aligned with those of their clientele. The aim of this study is to describe the demographic characteristics of current SLP students, to gauge whether the demographic composition of future clinicians is changing and is aligned with the population they are trained to serve. We designed an anonymous online survey, in the Canadian context, that enabled us to compare current SLP student demographics with statistics available about the target population at large. This survey was disseminated to all SLP students enrolled in an accredited institution in 2024. Participants answered questions about their age, sex and gender, linguistic, racial and cultural identities, and socioeconomic status. More than half (N=525, 53%) of currently enrolled SLP students completed the survey. Results indicate that SLP students are overwhelmingly cisgender females. SLP students spoke 48 unique languages, and while most were bi or multilingual, few felt competent enough in languages other than English and French to engage in clinical service delivery. There were 151 unique racial and ethnic identities reported, with the largest proportion of students identifying as “North American.” Culturally most SLPs identified as “Canadian.” Very few students identified as Black or Indigenous racially or culturally. Most respondents reported a higher-than-average total household income. Findings suggest that the demographic composition of current SLP students is more diverse than those currently practicing, however there is still over-, and underrepresentation of certain populations. Implications for practice and suggestions for future research 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".