Gender-affirming services in speech-language pathology: a survey of current practices
Bibliographic record
Abstract
Purpose: To investigate speech language pathologists’ (SLP) education on, knowledge of, familiarity with, and comfort/confidence in providing services to transgender and other gender-diverse individuals. Method: N=201 SLPs were surveyed online between December 2019 and March 2020, representing practitioners in the USA, Australia, Ireland, Canada, South Africa, and Italy. Empirical data was collected using a mixed-method online survey to evaluate trends in service provision to clients across the gender spectrum.Results: Most respondents identified as cisgender, were between the ages of 26-35 or over 45, and reported having worked with at least one gender-diverse client across their clinical career. SLPs working in facilities dedicated to gender-affirmative care were more likely to have worked with a member of our demographic of interest, and were also more likely to report strong confidence in use of demographic specific terms, like “gender fluid”, “gender dysphoria”, and “gender expression”, and were more likely to report consulting outside sources (conferences, colleagues, personal research) for further information on gender-diverse populations than those working in other spaces. Most respondents indicated hearing and knowing gender-relevant terminology, with mixed agreement about confidence in using said terms in a clinical setting. When asked about feeling confident and comfortable in clinically providing services to this population, the majority indicated that they would not. Conclusions: We discuss critical implications of the work as it pertains to current SLP practices, and also recommend future directions for the field, with the goal of moving toward a field-wide practice where all areas of service delivery are gender-inclusive and gender-affirmative.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".